These solutions are for reference only.

It is recommended that you should solve the assignments amd quizes by yourself honestly then only it makes sense to complete the course.
but if you are stuck in between refer these solutions

make sure you understand the solution
dont just copy paste it

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Assignment 1 - Introduction to Machine Learning

For this assignment, you will be using the Breast Cancer Wisconsin (Diagnostic) Database to create a classifier that can help diagnose patients. First, read through the description of the dataset (below).

import numpy as np import pandas as pd from sklearn.datasets import load_breast_cancer

cancer = load_breast_cancer()

print(cancer)

print(cancer.DESCR) # Print the data set description

The object returned by load_breast_cancer() is a scikit-learn Bunch object, which is similar to a dictionary.

import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer

cancer = load_breast_cancer()

# print(cancer)

print(cancer.DESCR) # Print the data set description


cancer.keys()

Breast Cancer Wisconsin (Diagnostic) Database

=============================================

Notes

-----

Data Set Characteristics:

:Number of Instances: 569

:Number of Attributes: 30 numeric, predictive attributes and the class

:Attribute Information:

- radius (mean of distances from center to points on the perimeter)

- texture (standard deviation of gray-scale values)

- perimeter

- area

- smoothness (local variation in radius lengths)

- compactness (perimeter^2 / area - 1.0)

- concavity (severity of concave portions of the contour)

- concave points (number of concave portions of the contour)

- symmetry

- fractal dimension ("coastline approximation" - 1)

The mean, standard error, and "worst" or largest (mean of the three

largest values) of these features were computed for each image,

resulting in 30 features. For instance, field 3 is Mean Radius, field

13 is Radius SE, field 23 is Worst Radius.

- class:

- WDBC-Malignant

- WDBC-Benign

:Summary Statistics:

===================================== ====== ======

Min Max

===================================== ====== ======

radius (mean): 6.981 28.11

texture (mean): 9.71 39.28

perimeter (mean): 43.79 188.5

area (mean): 143.5 2501.0

smoothness (mean): 0.053 0.163

compactness (mean): 0.019 0.345

concavity (mean): 0.0 0.427

concave points (mean): 0.0 0.201

symmetry (mean): 0.106 0.304

fractal dimension (mean): 0.05 0.097

radius (standard error): 0.112 2.873

texture (standard error): 0.36 4.885

perimeter (standard error): 0.757 21.98

area (standard error): 6.802 542.2

smoothness (standard error): 0.002 0.031

compactness (standard error): 0.002 0.135

concavity (standard error): 0.0 0.396

concave points (standard error): 0.0 0.053

symmetry (standard error): 0.008 0.079

fractal dimension (standard error): 0.001 0.03

radius (worst): 7.93 36.04

texture (worst): 12.02 49.54

perimeter (worst): 50.41 251.2

area (worst): 185.2 4254.0

smoothness (worst): 0.071 0.223

compactness (worst): 0.027 1.058

concavity (worst): 0.0 1.252

concave points (worst): 0.0 0.291

symmetry (worst): 0.156 0.664

fractal dimension (worst): 0.055 0.208

===================================== ====== ======

:Missing Attribute Values: None

:Class Distribution: 212 - Malignant, 357 - Benign

:Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian

:Donor: Nick Street

:Date: November, 1995

This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.

https://goo.gl/U2Uwz2

Features are computed from a digitized image of a fine needle

aspirate (FNA) of a breast mass. They describe

characteristics of the cell nuclei present in the image.

Separating plane described above was obtained using

Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree

Construction Via Linear Programming." Proceedings of the 4th

Midwest Artificial Intelligence and Cognitive Science Society,

pp. 97-101, 1992], a classification method which uses linear

programming to construct a decision tree. Relevant features

were selected using an exhaustive search in the space of 1-4

features and 1-3 separating planes.

The actual linear program used to obtain the separating plane

in the 3-dimensional space is that described in:

[K. P. Bennett and O. L. Mangasarian: "Robust Linear

Programming Discrimination of Two Linearly Inseparable Sets",

Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server:

ftp ftp.cs.wisc.edu

cd math-prog/cpo-dataset/machine-learn/WDBC/

References

----------

- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction

for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on

Electronic Imaging: Science and Technology, volume 1905, pages 861-870,

San Jose, CA, 1993.

- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and

prognosis via linear programming. Operations Research, 43(4), pages 570-577,

July-August 1995.

- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques

to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)

163-171.

Out[1]:

dict_keys(['target_names', 'data', 'target', 'feature_names', 'DESCR'])

Question 0 (Example)

How many features does the breast cancer dataset have?

This function should return an integer.

# You should write your whole answer within the function provided. The autograder will call
# this function and compare the return value against the correct solution value
def answer_zero():
    # This function returns the number of features of the breast cancer dataset, which is an integer. 
    # The assignment question description will tell you the general format the autograder is expecting
    return ((cancer['feature_names']))

# You can examine what your function returns by calling it in the cell. If you have questions
# about the assignment formats, check out the discussion forums for any FAQs
# ['target_names', 'data', 'target', 'feature_names', 'DESCR']

answer_zero()

Output:

array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',

'mean smoothness', 'mean compactness', 'mean concavity',

'mean concave points', 'mean symmetry', 'mean fractal dimension',

'radius error', 'texture error', 'perimeter error', 'area error',

'smoothness error', 'compactness error', 'concavity error',

'concave points error', 'symmetry error', 'fractal dimension error',

'worst radius', 'worst texture', 'worst perimeter', 'worst area',

'worst smoothness', 'worst compactness', 'worst concavity',

'worst concave points', 'worst symmetry', 'worst fractal dimension'],

dtype='<U23')

Question 1

Scikit-learn works with lists, numpy arrays, scipy-sparse matrices, and pandas DataFrames, so converting the dataset to a DataFrame is not necessary for training this model. Using a DataFrame does however help make many things easier such as munging data, so let's practice creating a classifier with a pandas DataFrame.

Convert the sklearn.dataset cancer to a DataFrame.

This function should return a (569, 31) DataFrame with

columns =

['mean radius', 'mean texture', 'mean perimeter', 'mean area',

'mean smoothness', 'mean compactness', 'mean concavity',

'mean concave points', 'mean symmetry', 'mean fractal dimension',

'radius error', 'texture error', 'perimeter error', 'area error',

'smoothness error', 'compactness error', 'concavity error',

'concave points error', 'symmetry error', 'fractal dimension error',

'worst radius', 'worst texture', 'worst perimeter', 'worst area',

'worst smoothness', 'worst compactness', 'worst concavity',

'worst concave points', 'worst symmetry', 'worst fractal dimension',

'target']

and index =

RangeIndex(start=0, stop=569, step=1)

def answer_one():
    
    # Your code here
#     print(len(cancer['feature_names']))
    columns = ['mean radius', 'mean texture', 'mean perimeter', 'mean area',
    'mean smoothness', 'mean compactness', 'mean concavity',
    'mean concave points', 'mean symmetry', 'mean fractal dimension',
    'radius error', 'texture error', 'perimeter error', 'area error',
    'smoothness error', 'compactness error', 'concavity error',
    'concave points error', 'symmetry error', 'fractal dimension error',
    'worst radius', 'worst texture', 'worst perimeter', 'worst area',
    'worst smoothness', 'worst compactness', 'worst concavity',
    'worst concave points', 'worst symmetry', 'worst fractal dimension',
    'target']
    
    
    index = range(0, 569, 1)
#     print(cancer['data'].shape)
    df = pd.DataFrame(data=cancer['data'], index=index, columns = columns[:30])
#     print(cancer['target'])
    df['target'] = cancer['target']
    
#     print(df.head())
    ans = df
    
    return ans


answer_one()

output:

	mean radius	mean texture	mean perimeter	mean area	mean smoothness	mean compactness	mean concavity	mean concave points	mean symmetry	mean fractal dimension	...	worst texture	worst perimeter	worst area	worst smoothness	worst compactness	worst concavity	worst concave points	worst symmetry	worst fractal dimension	target
0	17.990	10.38	122.80	1001.0	0.11840	0.27760	0.300100	0.147100	0.2419	0.07871	...	17.33	184.60	2019.0	0.16220	0.66560	0.71190	0.26540	0.4601	0.11890	0
1	20.570	17.77	132.90	1326.0	0.08474	0.07864	0.086900	0.070170	0.1812	0.05667	...	23.41	158.80	1956.0	0.12380	0.18660	0.24160	0.18600	0.2750	0.08902	0
2	19.690	21.25	130.00	1203.0	0.10960	0.15990	0.197400	0.127900	0.2069	0.05999	...	25.53	152.50	1709.0	0.14440	0.42450	0.45040	0.24300	0.3613	0.08758	0
3	11.420	20.38	77.58	386.1	0.14250	0.28390	0.241400	0.105200	0.2597	0.09744	...	26.50	98.87	567.7	0.20980	0.86630	0.68690	0.25750	0.6638	0.17300	0
4	20.290	14.34	135.10	1297.0	0.10030	0.13280	0.198000	0.104300	0.1809	0.05883	...	16.67	152.20	1575.0	0.13740	0.20500	0.40000	0.16250	0.2364	0.07678	0
5	12.450	15.70	82.57	477.1	0.12780	0.17000	0.157800	0.080890	0.2087	0.07613	...	23.75	103.40	741.6	0.17910	0.52490	0.53550	0.17410	0.3985	0.12440	0
6	18.250	19.98	119.60	1040.0	0.09463	0.10900	0.112700	0.074000	0.1794	0.05742	...	27.66	153.20	1606.0	0.14420	0.25760	0.37840	0.19320	0.3063	0.08368	0
7	13.710	20.83	90.20	577.9	0.11890	0.16450	0.093660	0.059850	0.2196	0.07451	...	28.14	110.60	897.0	0.16540	0.36820	0.26780	0.15560	0.3196	0.11510	0
8	13.000	21.82	87.50	519.8	0.12730	0.19320	0.185900	0.093530	0.2350	0.07389	...	30.73	106.20	739.3	0.17030	0.54010	0.53900	0.20600	0.4378	0.10720	0
9	12.460	24.04	83.97	475.9	0.11860	0.23960	0.227300	0.085430	0.2030	0.08243	...	40.68	97.65	711.4	0.18530	1.05800	1.10500	0.22100	0.4366	0.20750	0
10	16.020	23.24	102.70	797.8	0.08206	0.06669	0.032990	0.033230	0.1528	0.05697	...	33.88	123.80	1150.0	0.11810	0.15510	0.14590	0.09975	0.2948	0.08452	0
11	15.780	17.89	103.60	781.0	0.09710	0.12920	0.099540	0.066060	0.1842	0.06082	...	27.28	136.50	1299.0	0.13960	0.56090	0.39650	0.18100	0.3792	0.10480	0
12	19.170	24.80	132.40	1123.0	0.09740	0.24580	0.206500	0.111800	0.2397	0.07800	...	29.94	151.70	1332.0	0.10370	0.39030	0.36390	0.17670	0.3176	0.10230	0
13	15.850	23.95	103.70	782.7	0.08401	0.10020	0.099380	0.053640	0.1847	0.05338	...	27.66	112.00	876.5	0.11310	0.19240	0.23220	0.11190	0.2809	0.06287	0
14	13.730	22.61	93.60	578.3	0.11310	0.22930	0.212800	0.080250	0.2069	0.07682	...	32.01	108.80	697.7	0.16510	0.77250	0.69430	0.22080	0.3596	0.14310	0
15	14.540	27.54	96.73	658.8	0.11390	0.15950	0.163900	0.073640	0.2303	0.07077	...	37.13	124.10	943.2	0.16780	0.65770	0.70260	0.17120	0.4218	0.13410	0
16	14.680	20.13	94.74	684.5	0.09867	0.07200	0.073950	0.052590	0.1586	0.05922	...	30.88	123.40	1138.0	0.14640	0.18710	0.29140	0.16090	0.3029	0.08216	0
17	16.130	20.68	108.10	798.8	0.11700	0.20220	0.172200	0.102800	0.2164	0.07356	...	31.48	136.80	1315.0	0.17890	0.42330	0.47840	0.20730	0.3706	0.11420	0
18	19.810	22.15	130.00	1260.0	0.09831	0.10270	0.147900	0.094980	0.1582	0.05395	...	30.88	186.80	2398.0	0.15120	0.31500	0.53720	0.23880	0.2768	0.07615	0
19	13.540	14.36	87.46	566.3	0.09779	0.08129	0.066640	0.047810	0.1885	0.05766	...	19.26	99.70	711.2	0.14400	0.17730	0.23900	0.12880	0.2977	0.07259	1
20	13.080	15.71	85.63	520.0	0.10750	0.12700	0.045680	0.031100	0.1967	0.06811	...	20.49	96.09	630.5	0.13120	0.27760	0.18900	0.07283	0.3184	0.08183	1
21	9.504	12.44	60.34	273.9	0.10240	0.06492	0.029560	0.020760	0.1815	0.06905	...	15.66	65.13	314.9	0.13240	0.11480	0.08867	0.06227	0.2450	0.07773	1
22	15.340	14.26	102.50	704.4	0.10730	0.21350	0.207700	0.097560	0.2521	0.07032	...	19.08	125.10	980.9	0.13900	0.59540	0.63050	0.23930	0.4667	0.09946	0
23	21.160	23.04	137.20	1404.0	0.09428	0.10220	0.109700	0.086320	0.1769	0.05278	...	35.59	188.00	2615.0	0.14010	0.26000	0.31550	0.20090	0.2822	0.07526	0
24	16.650	21.38	110.00	904.6	0.11210	0.14570	0.152500	0.091700	0.1995	0.06330	...	31.56	177.00	2215.0	0.18050	0.35780	0.46950	0.20950	0.3613	0.09564	0
25	17.140	16.40	116.00	912.7	0.11860	0.22760	0.222900	0.140100	0.3040	0.07413	...	21.40	152.40	1461.0	0.15450	0.39490	0.38530	0.25500	0.4066	0.10590	0
26	14.580	21.53	97.41	644.8	0.10540	0.18680	0.142500	0.087830	0.2252	0.06924	...	33.21	122.40	896.9	0.15250	0.66430	0.55390	0.27010	0.4264	0.12750	0
27	18.610	20.25	122.10	1094.0	0.09440	0.10660	0.149000	0.077310	0.1697	0.05699	...	27.26	139.90	1403.0	0.13380	0.21170	0.34460	0.14900	0.2341	0.07421	0
28	15.300	25.27	102.40	732.4	0.10820	0.16970	0.168300	0.087510	0.1926	0.06540	...	36.71	149.30	1269.0	0.16410	0.61100	0.63350	0.20240	0.4027	0.09876	0
29	17.570	15.05	115.00	955.1	0.09847	0.11570	0.098750	0.079530	0.1739	0.06149	...	19.52	134.90	1227.0	0.12550	0.28120	0.24890	0.14560	0.2756	0.07919	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
539	7.691	25.44	48.34	170.4	0.08668	0.11990	0.092520	0.013640	0.2037	0.07751	...	31.89	54.49	223.6	0.15960	0.30640	0.33930	0.05000	0.2790	0.10660	1
540	11.540	14.44	74.65	402.9	0.09984	0.11200	0.067370	0.025940	0.1818	0.06782	...	19.68	78.78	457.8	0.13450	0.21180	0.17970	0.06918	0.2329	0.08134	1
541	14.470	24.99	95.81	656.4	0.08837	0.12300	0.100900	0.038900	0.1872	0.06341	...	31.73	113.50	808.9	0.13400	0.42020	0.40400	0.12050	0.3187	0.10230	1
542	14.740	25.42	94.70	668.6	0.08275	0.07214	0.041050	0.030270	0.1840	0.05680	...	32.29	107.40	826.4	0.10600	0.13760	0.16110	0.10950	0.2722	0.06956	1
543	13.210	28.06	84.88	538.4	0.08671	0.06877	0.029870	0.032750	0.1628	0.05781	...	37.17	92.48	629.6	0.10720	0.13810	0.10620	0.07958	0.2473	0.06443	1
544	13.870	20.70	89.77	584.8	0.09578	0.10180	0.036880	0.023690	0.1620	0.06688	...	24.75	99.17	688.6	0.12640	0.20370	0.13770	0.06845	0.2249	0.08492	1
545	13.620	23.23	87.19	573.2	0.09246	0.06747	0.029740	0.024430	0.1664	0.05801	...	29.09	97.58	729.8	0.12160	0.15170	0.10490	0.07174	0.2642	0.06953	1
546	10.320	16.35	65.31	324.9	0.09434	0.04994	0.010120	0.005495	0.1885	0.06201	...	21.77	71.12	384.9	0.12850	0.08842	0.04384	0.02381	0.2681	0.07399	1
547	10.260	16.58	65.85	320.8	0.08877	0.08066	0.043580	0.024380	0.1669	0.06714	...	22.04	71.08	357.4	0.14610	0.22460	0.17830	0.08333	0.2691	0.09479	1
548	9.683	19.34	61.05	285.7	0.08491	0.05030	0.023370	0.009615	0.1580	0.06235	...	25.59	69.10	364.2	0.11990	0.09546	0.09350	0.03846	0.2552	0.07920	1
549	10.820	24.21	68.89	361.6	0.08192	0.06602	0.015480	0.008160	0.1976	0.06328	...	31.45	83.90	505.6	0.12040	0.16330	0.06194	0.03264	0.3059	0.07626	1
550	10.860	21.48	68.51	360.5	0.07431	0.04227	0.000000	0.000000	0.1661	0.05948	...	24.77	74.08	412.3	0.10010	0.07348	0.00000	0.00000	0.2458	0.06592	1
551	11.130	22.44	71.49	378.4	0.09566	0.08194	0.048240	0.022570	0.2030	0.06552	...	28.26	77.80	436.6	0.10870	0.17820	0.15640	0.06413	0.3169	0.08032	1
552	12.770	29.43	81.35	507.9	0.08276	0.04234	0.019970	0.014990	0.1539	0.05637	...	36.00	88.10	594.7	0.12340	0.10640	0.08653	0.06498	0.2407	0.06484	1
553	9.333	21.94	59.01	264.0	0.09240	0.05605	0.039960	0.012820	0.1692	0.06576	...	25.05	62.86	295.8	0.11030	0.08298	0.07993	0.02564	0.2435	0.07393	1
554	12.880	28.92	82.50	514.3	0.08123	0.05824	0.061950	0.023430	0.1566	0.05708	...	35.74	88.84	595.7	0.12270	0.16200	0.24390	0.06493	0.2372	0.07242	1
555	10.290	27.61	65.67	321.4	0.09030	0.07658	0.059990	0.027380	0.1593	0.06127	...	34.91	69.57	357.6	0.13840	0.17100	0.20000	0.09127	0.2226	0.08283	1
556	10.160	19.59	64.73	311.7	0.10030	0.07504	0.005025	0.011160	0.1791	0.06331	...	22.88	67.88	347.3	0.12650	0.12000	0.01005	0.02232	0.2262	0.06742	1
557	9.423	27.88	59.26	271.3	0.08123	0.04971	0.000000	0.000000	0.1742	0.06059	...	34.24	66.50	330.6	0.10730	0.07158	0.00000	0.00000	0.2475	0.06969	1
558	14.590	22.68	96.39	657.1	0.08473	0.13300	0.102900	0.037360	0.1454	0.06147	...	27.27	105.90	733.5	0.10260	0.31710	0.36620	0.11050	0.2258	0.08004	1
559	11.510	23.93	74.52	403.5	0.09261	0.10210	0.111200	0.041050	0.1388	0.06570	...	37.16	82.28	474.2	0.12980	0.25170	0.36300	0.09653	0.2112	0.08732	1
560	14.050	27.15	91.38	600.4	0.09929	0.11260	0.044620	0.043040	0.1537	0.06171	...	33.17	100.20	706.7	0.12410	0.22640	0.13260	0.10480	0.2250	0.08321	1
561	11.200	29.37	70.67	386.0	0.07449	0.03558	0.000000	0.000000	0.1060	0.05502	...	38.30	75.19	439.6	0.09267	0.05494	0.00000	0.00000	0.1566	0.05905	1
562	15.220	30.62	103.40	716.9	0.10480	0.20870	0.255000	0.094290	0.2128	0.07152	...	42.79	128.70	915.0	0.14170	0.79170	1.17000	0.23560	0.4089	0.14090	0
563	20.920	25.09	143.00	1347.0	0.10990	0.22360	0.317400	0.147400	0.2149	0.06879	...	29.41	179.10	1819.0	0.14070	0.41860	0.65990	0.25420	0.2929	0.09873	0
564	21.560	22.39	142.00	1479.0	0.11100	0.11590	0.243900	0.138900	0.1726	0.05623	...	26.40	166.10	2027.0	0.14100	0.21130	0.41070	0.22160	0.2060	0.07115	0
565	20.130	28.25	131.20	1261.0	0.09780	0.10340	0.144000	0.097910	0.1752	0.05533	...	38.25	155.00	1731.0	0.11660	0.19220	0.32150	0.16280	0.2572	0.06637	0
566	16.600	28.08	108.30	858.1	0.08455	0.10230	0.092510	0.053020	0.1590	0.05648	...	34.12	126.70	1124.0	0.11390	0.30940	0.34030	0.14180	0.2218	0.07820	0
567	20.600	29.33	140.10	1265.0	0.11780	0.27700	0.351400	0.152000	0.2397	0.07016	...	39.42	184.60	1821.0	0.16500	0.86810	0.93870	0.26500	0.4087	0.12400	0
568	7.760	24.54	47.92	181.0	0.05263	0.04362	0.000000	0.000000	0.1587	0.05884	...	30.37	59.16	268.6	0.08996	0.06444	0.00000	0.00000	0.2871	0.07039	1

569 rows × 31 columns

Question 2

What is the class distribution? (i.e. how many instances of malignant (encoded 0) and how many benign (encoded 1)?)

This function should return a Series named target of length 2 with integer values and index = ['malignant', 'benign']

def answer_two():
    cancerdf = answer_one()
    
#     cancerdf = pd.DataFrame(cancerdf)
    
    # Your code here
    malignant_count = len(cancerdf[cancerdf['target'] == 0])
    benign_count = len(cancerdf[cancerdf['target'] == 1])
    
    index = ['malignant', 'benign']
    
    target = pd.Series(data=[malignant_count, benign_count], index=index)
    
#     print(target)
    ans = target
    
    
    return ans


answer_two()

Output:

malignant 212

benign 357

dtype: int64

Question 3

Split the DataFrame into X (the data) and y (the labels).

This function should return a tuple of length 2: (X, y), where

X has shape (569, 30)

y has shape (569,).

def answer_three():
    cancerdf = answer_one()
#     from sklearn.model_selection import train_test_split
    
#     # Your code here
#     X = fruits[['height', 'width', 'mass', 'color_score']]
#     y = fruits['fruit_label']
#     X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    
#     return X, y
    X = cancerdf.iloc[:,:30]
    y = cancerdf.iloc[:,30:32]
#     y = cancerdf['target']
    y = cancerdf.target
#     print(y)
    return X, y

answer_three()

Output:

( mean radius mean texture mean perimeter mean area mean smoothness \

0 17.990 10.38 122.80 1001.0 0.11840

1 20.570 17.77 132.90 1326.0 0.08474

2 19.690 21.25 130.00 1203.0 0.10960

3 11.420 20.38 77.58 386.1 0.14250

4 20.290 14.34 135.10 1297.0 0.10030

5 12.450 15.70 82.57 477.1 0.12780

6 18.250 19.98 119.60 1040.0 0.09463

7 13.710 20.83 90.20 577.9 0.11890

8 13.000 21.82 87.50 519.8 0.12730

9 12.460 24.04 83.97 475.9 0.11860

10 16.020 23.24 102.70 797.8 0.08206

11 15.780 17.89 103.60 781.0 0.09710

12 19.170 24.80 132.40 1123.0 0.09740

13 15.850 23.95 103.70 782.7 0.08401

14 13.730 22.61 93.60 578.3 0.11310

15 14.540 27.54 96.73 658.8 0.11390

16 14.680 20.13 94.74 684.5 0.09867

17 16.130 20.68 108.10 798.8 0.11700

18 19.810 22.15 130.00 1260.0 0.09831

19 13.540 14.36 87.46 566.3 0.09779

20 13.080 15.71 85.63 520.0 0.10750

21 9.504 12.44 60.34 273.9 0.10240

22 15.340 14.26 102.50 704.4 0.10730

23 21.160 23.04 137.20 1404.0 0.09428

24 16.650 21.38 110.00 904.6 0.11210

25 17.140 16.40 116.00 912.7 0.11860

26 14.580 21.53 97.41 644.8 0.10540

27 18.610 20.25 122.10 1094.0 0.09440

28 15.300 25.27 102.40 732.4 0.10820

29 17.570 15.05 115.00 955.1 0.09847

.. ... ... ... ... ...

539 7.691 25.44 48.34 170.4 0.08668

540 11.540 14.44 74.65 402.9 0.09984

541 14.470 24.99 95.81 656.4 0.08837

542 14.740 25.42 94.70 668.6 0.08275

543 13.210 28.06 84.88 538.4 0.08671

544 13.870 20.70 89.77 584.8 0.09578

545 13.620 23.23 87.19 573.2 0.09246

546 10.320 16.35 65.31 324.9 0.09434

547 10.260 16.58 65.85 320.8 0.08877

548 9.683 19.34 61.05 285.7 0.08491

549 10.820 24.21 68.89 361.6 0.08192

550 10.860 21.48 68.51 360.5 0.07431

551 11.130 22.44 71.49 378.4 0.09566

552 12.770 29.43 81.35 507.9 0.08276

553 9.333 21.94 59.01 264.0 0.09240

554 12.880 28.92 82.50 514.3 0.08123

555 10.290 27.61 65.67 321.4 0.09030

556 10.160 19.59 64.73 311.7 0.10030

557 9.423 27.88 59.26 271.3 0.08123

558 14.590 22.68 96.39 657.1 0.08473

559 11.510 23.93 74.52 403.5 0.09261

560 14.050 27.15 91.38 600.4 0.09929

561 11.200 29.37 70.67 386.0 0.07449

562 15.220 30.62 103.40 716.9 0.10480

563 20.920 25.09 143.00 1347.0 0.10990

564 21.560 22.39 142.00 1479.0 0.11100

565 20.130 28.25 131.20 1261.0 0.09780

566 16.600 28.08 108.30 858.1 0.08455

567 20.600 29.33 140.10 1265.0 0.11780

568 7.760 24.54 47.92 181.0 0.05263

mean compactness mean concavity mean concave points mean symmetry \

0 0.27760 0.300100 0.147100 0.2419

1 0.07864 0.086900 0.070170 0.1812

2 0.15990 0.197400 0.127900 0.2069

3 0.28390 0.241400 0.105200 0.2597

4 0.13280 0.198000 0.104300 0.1809

5 0.17000 0.157800 0.080890 0.2087

6 0.10900 0.112700 0.074000 0.1794

7 0.16450 0.093660 0.059850 0.2196

8 0.19320 0.185900 0.093530 0.2350

9 0.23960 0.227300 0.085430 0.2030

10 0.06669 0.032990 0.033230 0.1528

11 0.12920 0.099540 0.066060 0.1842

12 0.24580 0.206500 0.111800 0.2397

13 0.10020 0.099380 0.053640 0.1847

14 0.22930 0.212800 0.080250 0.2069

15 0.15950 0.163900 0.073640 0.2303

16 0.07200 0.073950 0.052590 0.1586

17 0.20220 0.172200 0.102800 0.2164

18 0.10270 0.147900 0.094980 0.1582

19 0.08129 0.066640 0.047810 0.1885

20 0.12700 0.045680 0.031100 0.1967

21 0.06492 0.029560 0.020760 0.1815

22 0.21350 0.207700 0.097560 0.2521

23 0.10220 0.109700 0.086320 0.1769

24 0.14570 0.152500 0.091700 0.1995

25 0.22760 0.222900 0.140100 0.3040

26 0.18680 0.142500 0.087830 0.2252

27 0.10660 0.149000 0.077310 0.1697

28 0.16970 0.168300 0.087510 0.1926

29 0.11570 0.098750 0.079530 0.1739

.. ... ... ... ...

539 0.11990 0.092520 0.013640 0.2037

540 0.11200 0.067370 0.025940 0.1818

541 0.12300 0.100900 0.038900 0.1872

542 0.07214 0.041050 0.030270 0.1840

543 0.06877 0.029870 0.032750 0.1628

544 0.10180 0.036880 0.023690 0.1620

545 0.06747 0.029740 0.024430 0.1664

546 0.04994 0.010120 0.005495 0.1885

547 0.08066 0.043580 0.024380 0.1669

548 0.05030 0.023370 0.009615 0.1580

549 0.06602 0.015480 0.008160 0.1976

550 0.04227 0.000000 0.000000 0.1661

551 0.08194 0.048240 0.022570 0.2030

552 0.04234 0.019970 0.014990 0.1539

553 0.05605 0.039960 0.012820 0.1692

554 0.05824 0.061950 0.023430 0.1566

555 0.07658 0.059990 0.027380 0.1593

556 0.07504 0.005025 0.011160 0.1791

557 0.04971 0.000000 0.000000 0.1742

558 0.13300 0.102900 0.037360 0.1454

559 0.10210 0.111200 0.041050 0.1388

560 0.11260 0.044620 0.043040 0.1537

561 0.03558 0.000000 0.000000 0.1060

562 0.20870 0.255000 0.094290 0.2128

563 0.22360 0.317400 0.147400 0.2149

564 0.11590 0.243900 0.138900 0.1726

565 0.10340 0.144000 0.097910 0.1752

566 0.10230 0.092510 0.053020 0.1590

567 0.27700 0.351400 0.152000 0.2397

568 0.04362 0.000000 0.000000 0.1587

mean fractal dimension ... worst radius \

0 0.07871 ... 25.380

1 0.05667 ... 24.990

2 0.05999 ... 23.570

3 0.09744 ... 14.910

4 0.05883 ... 22.540

5 0.07613 ... 15.470

6 0.05742 ... 22.880

7 0.07451 ... 17.060

8 0.07389 ... 15.490

9 0.08243 ... 15.090

10 0.05697 ... 19.190

11 0.06082 ... 20.420

12 0.07800 ... 20.960

13 0.05338 ... 16.840

14 0.07682 ... 15.030

15 0.07077 ... 17.460

16 0.05922 ... 19.070

17 0.07356 ... 20.960

18 0.05395 ... 27.320

19 0.05766 ... 15.110

20 0.06811 ... 14.500

21 0.06905 ... 10.230

22 0.07032 ... 18.070

23 0.05278 ... 29.170

24 0.06330 ... 26.460

25 0.07413 ... 22.250

26 0.06924 ... 17.620

27 0.05699 ... 21.310

28 0.06540 ... 20.270

29 0.06149 ... 20.010

.. ... ... ...

539 0.07751 ... 8.678

540 0.06782 ... 12.260

541 0.06341 ... 16.220

542 0.05680 ... 16.510

543 0.05781 ... 14.370

544 0.06688 ... 15.050

545 0.05801 ... 15.350

546 0.06201 ... 11.250

547 0.06714 ... 10.830

548 0.06235 ... 10.930

549 0.06328 ... 13.030

550 0.05948 ... 11.660

551 0.06552 ... 12.020

552 0.05637 ... 13.870

553 0.06576 ... 9.845

554 0.05708 ... 13.890

555 0.06127 ... 10.840

556 0.06331 ... 10.650

557 0.06059 ... 10.490

558 0.06147 ... 15.480

559 0.06570 ... 12.480

560 0.06171 ... 15.300

561 0.05502 ... 11.920

562 0.07152 ... 17.520

563 0.06879 ... 24.290

564 0.05623 ... 25.450

565 0.05533 ... 23.690

566 0.05648 ... 18.980

567 0.07016 ... 25.740

568 0.05884 ... 9.456

worst texture worst perimeter worst area worst smoothness \

0 17.33 184.60 2019.0 0.16220

1 23.41 158.80 1956.0 0.12380

2 25.53 152.50 1709.0 0.14440

3 26.50 98.87 567.7 0.20980

4 16.67 152.20 1575.0 0.13740

5 23.75 103.40 741.6 0.17910

6 27.66 153.20 1606.0 0.14420

7 28.14 110.60 897.0 0.16540

8 30.73 106.20 739.3 0.17030

9 40.68 97.65 711.4 0.18530

10 33.88 123.80 1150.0 0.11810

11 27.28 136.50 1299.0 0.13960

12 29.94 151.70 1332.0 0.10370

13 27.66 112.00 876.5 0.11310

14 32.01 108.80 697.7 0.16510

15 37.13 124.10 943.2 0.16780

16 30.88 123.40 1138.0 0.14640

17 31.48 136.80 1315.0 0.17890

18 30.88 186.80 2398.0 0.15120

19 19.26 99.70 711.2 0.14400

20 20.49 96.09 630.5 0.13120

21 15.66 65.13 314.9 0.13240

22 19.08 125.10 980.9 0.13900

23 35.59 188.00 2615.0 0.14010

24 31.56 177.00 2215.0 0.18050

25 21.40 152.40 1461.0 0.15450

26 33.21 122.40 896.9 0.15250

27 27.26 139.90 1403.0 0.13380

28 36.71 149.30 1269.0 0.16410

29 19.52 134.90 1227.0 0.12550

.. ... ... ... ...

539 31.89 54.49 223.6 0.15960

540 19.68 78.78 457.8 0.13450

541 31.73 113.50 808.9 0.13400

542 32.29 107.40 826.4 0.10600

543 37.17 92.48 629.6 0.10720

544 24.75 99.17 688.6 0.12640

545 29.09 97.58 729.8 0.12160

546 21.77 71.12 384.9 0.12850

547 22.04 71.08 357.4 0.14610

548 25.59 69.10 364.2 0.11990

549 31.45 83.90 505.6 0.12040

550 24.77 74.08 412.3 0.10010

551 28.26 77.80 436.6 0.10870

552 36.00 88.10 594.7 0.12340

553 25.05 62.86 295.8 0.11030

554 35.74 88.84 595.7 0.12270

555 34.91 69.57 357.6 0.13840

556 22.88 67.88 347.3 0.12650

557 34.24 66.50 330.6 0.10730

558 27.27 105.90 733.5 0.10260

559 37.16 82.28 474.2 0.12980

560 33.17 100.20 706.7 0.12410

561 38.30 75.19 439.6 0.09267

562 42.79 128.70 915.0 0.14170

563 29.41 179.10 1819.0 0.14070

564 26.40 166.10 2027.0 0.14100

565 38.25 155.00 1731.0 0.11660

566 34.12 126.70 1124.0 0.11390

567 39.42 184.60 1821.0 0.16500

568 30.37 59.16 268.6 0.08996

worst compactness worst concavity worst concave points worst symmetry \

0 0.66560 0.71190 0.26540 0.4601

1 0.18660 0.24160 0.18600 0.2750

2 0.42450 0.45040 0.24300 0.3613

3 0.86630 0.68690 0.25750 0.6638

4 0.20500 0.40000 0.16250 0.2364

5 0.52490 0.53550 0.17410 0.3985

6 0.25760 0.37840 0.19320 0.3063

7 0.36820 0.26780 0.15560 0.3196

8 0.54010 0.53900 0.20600 0.4378

9 1.05800 1.10500 0.22100 0.4366

10 0.15510 0.14590 0.09975 0.2948

11 0.56090 0.39650 0.18100 0.3792

12 0.39030 0.36390 0.17670 0.3176

13 0.19240 0.23220 0.11190 0.2809

14 0.77250 0.69430 0.22080 0.3596

15 0.65770 0.70260 0.17120 0.4218

16 0.18710 0.29140 0.16090 0.3029

17 0.42330 0.47840 0.20730 0.3706

18 0.31500 0.53720 0.23880 0.2768

19 0.17730 0.23900 0.12880 0.2977

20 0.27760 0.18900 0.07283 0.3184

21 0.11480 0.08867 0.06227 0.2450

22 0.59540 0.63050 0.23930 0.4667

23 0.26000 0.31550 0.20090 0.2822

24 0.35780 0.46950 0.20950 0.3613

25 0.39490 0.38530 0.25500 0.4066

26 0.66430 0.55390 0.27010 0.4264

27 0.21170 0.34460 0.14900 0.2341

28 0.61100 0.63350 0.20240 0.4027

29 0.28120 0.24890 0.14560 0.2756

.. ... ... ... ...

539 0.30640 0.33930 0.05000 0.2790

540 0.21180 0.17970 0.06918 0.2329

541 0.42020 0.40400 0.12050 0.3187

542 0.13760 0.16110 0.10950 0.2722

543 0.13810 0.10620 0.07958 0.2473

544 0.20370 0.13770 0.06845 0.2249

545 0.15170 0.10490 0.07174 0.2642

546 0.08842 0.04384 0.02381 0.2681

547 0.22460 0.17830 0.08333 0.2691

548 0.09546 0.09350 0.03846 0.2552

549 0.16330 0.06194 0.03264 0.3059

550 0.07348 0.00000 0.00000 0.2458

551 0.17820 0.15640 0.06413 0.3169

552 0.10640 0.08653 0.06498 0.2407

553 0.08298 0.07993 0.02564 0.2435

554 0.16200 0.24390 0.06493 0.2372

555 0.17100 0.20000 0.09127 0.2226

556 0.12000 0.01005 0.02232 0.2262

557 0.07158 0.00000 0.00000 0.2475

558 0.31710 0.36620 0.11050 0.2258

559 0.25170 0.36300 0.09653 0.2112

560 0.22640 0.13260 0.10480 0.2250

561 0.05494 0.00000 0.00000 0.1566

562 0.79170 1.17000 0.23560 0.4089

563 0.41860 0.65990 0.25420 0.2929

564 0.21130 0.41070 0.22160 0.2060

565 0.19220 0.32150 0.16280 0.2572

566 0.30940 0.34030 0.14180 0.2218

567 0.86810 0.93870 0.26500 0.4087

568 0.06444 0.00000 0.00000 0.2871

worst fractal dimension

0 0.11890

1 0.08902

2 0.08758

3 0.17300

4 0.07678

5 0.12440

6 0.08368

7 0.11510

8 0.10720

9 0.20750

10 0.08452

11 0.10480

12 0.10230

13 0.06287

14 0.14310

15 0.13410

16 0.08216

17 0.11420

18 0.07615

19 0.07259

20 0.08183

21 0.07773

22 0.09946

23 0.07526

24 0.09564

25 0.10590

26 0.12750

27 0.07421

28 0.09876

29 0.07919

.. ...

539 0.10660

540 0.08134

541 0.10230

542 0.06956

543 0.06443

544 0.08492

545 0.06953

546 0.07399

547 0.09479

548 0.07920

549 0.07626

550 0.06592

551 0.08032

552 0.06484

553 0.07393

554 0.07242

555 0.08283

556 0.06742

557 0.06969

558 0.08004

559 0.08732

560 0.08321

561 0.05905

562 0.14090

563 0.09873

564 0.07115

565 0.06637

566 0.07820

567 0.12400

568 0.07039

[569 rows x 30 columns], 0 0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

9 0

10 0

11 0

12 0

13 0

14 0

15 0

16 0

17 0

18 0

19 1

20 1

21 1

22 0

23 0

24 0

25 0

26 0

27 0

28 0

29 0

539 1

540 1

541 1

542 1

543 1

544 1

545 1

546 1

547 1

548 1

549 1

550 1

551 1

552 1

553 1

554 1

555 1

556 1

557 1

558 1

559 1

560 1

561 1

562 0

563 0

564 0

565 0

566 0

567 0

568 1

Name: target, dtype: int64)

Question 4

Using train_test_split, split X and y into training and test sets (X_train, X_test, y_train, and y_test).

Set the random number generator state to 0 using random_state=0 to make sure your results match the autograder!

This function should return a tuple of length 4: (X_train, X_test, y_train, y_test), where

X_train has shape (426, 30)

X_test has shape (143, 30)

y_train has shape (426,)

y_test has shape (143,)

from sklearn.model_selection import train_test_split

def answer_four():
    X, y = answer_three()
    
    # Your code here
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
#     print(y_test.shape)
    
#     return 'ans'
    return X_train, X_test, y_train, y_test
answer_four()

Out[80]:

( mean radius mean texture mean perimeter mean area mean smoothness \

293 11.850 17.46 75.54 432.7 0.08372

332 11.220 19.86 71.94 387.3 0.10540

565 20.130 28.25 131.20 1261.0 0.09780

278 13.590 17.84 86.24 572.3 0.07948

489 16.690 20.20 107.10 857.6 0.07497

346 12.060 18.90 76.66 445.3 0.08386

357 13.870 16.21 88.52 593.7 0.08743

355 12.560 19.07 81.92 485.8 0.08760

112 14.260 19.65 97.83 629.9 0.07837

68 9.029 17.33 58.79 250.5 0.10660

526 13.460 18.75 87.44 551.1 0.10750

206 9.876 17.27 62.92 295.4 0.10890

65 14.780 23.94 97.40 668.3 0.11720

437 14.040 15.98 89.78 611.2 0.08458

126 13.610 24.69 87.76 572.6 0.09258

429 12.720 17.67 80.98 501.3 0.07896

392 15.490 19.97 102.40 744.7 0.11600

343 19.680 21.68 129.90 1194.0 0.09797

334 12.300 19.02 77.88 464.4 0.08313

440 10.970 17.20 71.73 371.5 0.08915

441 17.270 25.42 112.40 928.8 0.08331

137 11.430 15.39 73.06 399.8 0.09639

230 17.050 19.08 113.40 895.0 0.11410

7 13.710 20.83 90.20 577.9 0.11890

408 17.990 20.66 117.80 991.7 0.10360

523 13.710 18.68 88.73 571.0 0.09916

361 13.300 21.57 85.24 546.1 0.08582

553 9.333 21.94 59.01 264.0 0.09240

478 11.490 14.59 73.99 404.9 0.10460

303 10.490 18.61 66.86 334.3 0.10680

.. ... ... ... ... ...

459 9.755 28.20 61.68 290.9 0.07984

510 11.740 14.69 76.31 426.0 0.08099

151 8.219 20.70 53.27 203.9 0.09405

244 19.400 23.50 129.10 1155.0 0.10270

543 13.210 28.06 84.88 538.4 0.08671

544 13.870 20.70 89.77 584.8 0.09578

265 20.730 31.12 135.70 1419.0 0.09469

288 11.260 19.96 73.72 394.1 0.08020

423 13.660 19.13 89.46 575.3 0.09057

147 14.950 18.77 97.84 689.5 0.08138

177 16.460 20.11 109.30 832.9 0.09831

99 14.420 19.77 94.48 642.5 0.09752

448 14.530 19.34 94.25 659.7 0.08388

431 12.400 17.68 81.47 467.8 0.10540

115 11.930 21.53 76.53 438.6 0.09768

72 17.200 24.52 114.20 929.4 0.10710

537 11.690 24.44 76.37 406.4 0.12360

174 10.660 15.15 67.49 349.6 0.08792

87 19.020 24.59 122.00 1076.0 0.09029

551 11.130 22.44 71.49 378.4 0.09566

486 14.640 16.85 94.21 666.0 0.08641

314 8.597 18.60 54.09 221.2 0.10740

396 13.510 18.89 88.10 558.1 0.10590

472 14.920 14.93 96.45 686.9 0.08098

70 18.940 21.31 123.60 1130.0 0.09009

277 18.810 19.98 120.90 1102.0 0.08923

9 12.460 24.04 83.97 475.9 0.11860

359 9.436 18.32 59.82 278.6 0.10090

192 9.720 18.22 60.73 288.1 0.06950

559 11.510 23.93 74.52 403.5 0.09261

mean compactness mean concavity mean concave points mean symmetry \

293 0.05642 0.026880 0.022800 0.1875

332 0.06779 0.005006 0.007583 0.1940

565 0.10340 0.144000 0.097910 0.1752

278 0.04052 0.019970 0.012380 0.1573

489 0.07112 0.036490 0.023070 0.1846

346 0.05794 0.007510 0.008488 0.1555

357 0.05492 0.015020 0.020880 0.1424

355 0.10380 0.103000 0.043910 0.1533

112 0.22330 0.300300 0.077980 0.1704

68 0.14130 0.313000 0.043750 0.2111

526 0.11380 0.042010 0.031520 0.1723

206 0.07232 0.017560 0.019520 0.1934

65 0.14790 0.126700 0.090290 0.1953

437 0.05895 0.035340 0.029440 0.1714

126 0.07862 0.052850 0.030850 0.1761

429 0.04522 0.014020 0.018350 0.1459

392 0.15620 0.189100 0.091130 0.1929

343 0.13390 0.186300 0.110300 0.2082

334 0.04202 0.007756 0.008535 0.1539

440 0.11130 0.094570 0.036130 0.1489

441 0.11090 0.120400 0.057360 0.1467

137 0.06889 0.035030 0.028750 0.1734

230 0.15720 0.191000 0.109000 0.2131

7 0.16450 0.093660 0.059850 0.2196

408 0.13040 0.120100 0.088240 0.1992

523 0.10700 0.053850 0.037830 0.1714

361 0.06373 0.033440 0.024240 0.1815

553 0.05605 0.039960 0.012820 0.1692

478 0.08228 0.053080 0.019690 0.1779

303 0.06678 0.022970 0.017800 0.1482

.. ... ... ... ...

459 0.04626 0.015410 0.010430 0.1621

510 0.09661 0.067260 0.026390 0.1499

151 0.13050 0.132100 0.021680 0.2222

244 0.15580 0.204900 0.088860 0.1978

543 0.06877 0.029870 0.032750 0.1628

544 0.10180 0.036880 0.023690 0.1620

265 0.11430 0.136700 0.086460 0.1769

288 0.11810 0.092740 0.055880 0.2595

423 0.11470 0.096570 0.048120 0.1848

147 0.11670 0.090500 0.035620 0.1744

177 0.15560 0.179300 0.088660 0.1794

99 0.11410 0.093880 0.058390 0.1879

448 0.07800 0.088170 0.029250 0.1473

431 0.13160 0.077410 0.027990 0.1811

115 0.07849 0.033280 0.020080 0.1688

72 0.18300 0.169200 0.079440 0.1927

537 0.15520 0.045150 0.045310 0.2131

174 0.04302 0.000000 0.000000 0.1928

87 0.12060 0.146800 0.082710 0.1953

551 0.08194 0.048240 0.022570 0.2030

486 0.06698 0.051920 0.027910 0.1409

314 0.05847 0.000000 0.000000 0.2163

396 0.11470 0.085800 0.053810 0.1806

472 0.08549 0.055390 0.032210 0.1687

70 0.10290 0.108000 0.079510 0.1582

277 0.05884 0.080200 0.058430 0.1550

9 0.23960 0.227300 0.085430 0.2030

359 0.05956 0.027100 0.014060 0.1506

192 0.02344 0.000000 0.000000 0.1653

559 0.10210 0.111200 0.041050 0.1388

mean fractal dimension ... worst radius \

293 0.05715 ... 13.060

332 0.06028 ... 11.980

565 0.05533 ... 23.690

278 0.05520 ... 15.500

489 0.05325 ... 19.180

346 0.06048 ... 13.640

357 0.05883 ... 15.110

355 0.06184 ... 13.370

112 0.07769 ... 15.300

68 0.08046 ... 10.310

526 0.06317 ... 15.350

206 0.06285 ... 10.420

65 0.06654 ... 17.310

437 0.05898 ... 15.660

126 0.06130 ... 16.890

429 0.05544 ... 13.820

392 0.06744 ... 21.200

343 0.05715 ... 22.750

334 0.05945 ... 13.350

440 0.06640 ... 12.360

441 0.05407 ... 20.380

137 0.05865 ... 12.320

230 0.06325 ... 19.590

7 0.07451 ... 17.060

408 0.06069 ... 21.080

523 0.06843 ... 15.110

361 0.05696 ... 14.200

553 0.06576 ... 9.845

478 0.06574 ... 12.400

303 0.06600 ... 11.060

.. ... ... ...

459 0.05952 ... 10.670

510 0.06758 ... 12.450

151 0.08261 ... 9.092

244 0.06000 ... 21.650

543 0.05781 ... 14.370

544 0.06688 ... 15.050

265 0.05674 ... 32.490

288 0.06233 ... 11.860

423 0.06181 ... 15.140

147 0.06493 ... 16.250

177 0.06323 ... 17.790

99 0.06390 ... 16.330

448 0.05746 ... 16.300

431 0.07102 ... 12.880

115 0.06194 ... 13.670

72 0.06487 ... 23.320

537 0.07405 ... 12.980

174 0.05975 ... 11.540

87 0.05629 ... 24.560

551 0.06552 ... 12.020

486 0.05355 ... 16.460

314 0.07359 ... 8.952

396 0.06079 ... 14.800

472 0.05669 ... 17.180

70 0.05461 ... 24.860

277 0.04996 ... 19.960

9 0.08243 ... 15.090

359 0.06959 ... 12.020

192 0.06447 ... 9.968

559 0.06570 ... 12.480

worst texture worst perimeter worst area worst smoothness \

293 25.75 84.35 517.8 0.13690

332 25.78 76.91 436.1 0.14240

565 38.25 155.00 1731.0 0.11660

278 26.10 98.91 739.1 0.10500

489 26.56 127.30 1084.0 0.10090

346 27.06 86.54 562.6 0.12890

357 25.58 96.74 694.4 0.11530

355 22.43 89.02 547.4 0.10960

112 23.73 107.00 709.0 0.08949

68 22.65 65.50 324.7 0.14820

526 25.16 101.90 719.8 0.16240

206 23.22 67.08 331.6 0.14150

65 33.39 114.60 925.1 0.16480

437 21.58 101.20 750.0 0.11950

126 35.64 113.20 848.7 0.14710

429 20.96 88.87 586.8 0.10680

392 29.41 142.10 1359.0 0.16810

343 34.66 157.60 1540.0 0.12180

334 28.46 84.53 544.3 0.12220

440 26.87 90.14 476.4 0.13910

441 35.46 132.80 1284.0 0.14360

137 22.02 79.93 462.0 0.11900

230 24.89 133.50 1189.0 0.17030

7 28.14 110.60 897.0 0.16540

408 25.41 138.10 1349.0 0.14820

523 25.63 99.43 701.9 0.14250

361 29.20 92.94 621.2 0.11400

553 25.05 62.86 295.8 0.11030

478 21.90 82.04 467.6 0.13520

303 24.54 70.76 375.4 0.14130

.. ... ... ... ...

459 36.92 68.03 349.9 0.11100

510 17.60 81.25 473.8 0.10730

151 29.72 58.08 249.8 0.16300

244 30.53 144.90 1417.0 0.14630

543 37.17 92.48 629.6 0.10720

544 24.75 99.17 688.6 0.12640

265 47.16 214.00 3432.0 0.14010

288 22.33 78.27 437.6 0.10280

423 25.50 101.40 708.8 0.11470

147 25.47 107.10 809.7 0.09970

177 28.45 123.50 981.2 0.14150

99 30.86 109.50 826.4 0.14310

448 28.39 108.10 830.5 0.10890

431 22.91 89.61 515.8 0.14500

115 26.15 87.54 583.0 0.15000

72 33.82 151.60 1681.0 0.15850

537 32.19 86.12 487.7 0.17680

174 19.20 73.20 408.3 0.10760

87 30.41 152.90 1623.0 0.12490

551 28.26 77.80 436.6 0.10870

486 25.44 106.00 831.0 0.11420

314 22.44 56.65 240.1 0.13470

396 27.20 97.33 675.2 0.14280

472 18.22 112.00 906.6 0.10650

70 26.58 165.90 1866.0 0.11930

277 24.30 129.00 1236.0 0.12430

9 40.68 97.65 711.4 0.18530

359 25.02 75.79 439.6 0.13330

192 20.83 62.25 303.8 0.07117

559 37.16 82.28 474.2 0.12980

worst compactness worst concavity worst concave points worst symmetry \

293 0.17580 0.13160 0.09140 0.3101

332 0.09669 0.01335 0.02022 0.3292

565 0.19220 0.32150 0.16280 0.2572

278 0.07622 0.10600 0.05185 0.2335

489 0.29200 0.24770 0.08737 0.4677

346 0.13520 0.04506 0.05093 0.2880

357 0.10080 0.05285 0.05556 0.2362

355 0.20020 0.23880 0.09265 0.2121

112 0.41930 0.67830 0.15050 0.2398

68 0.43650 1.25200 0.17500 0.4228

526 0.31240 0.26540 0.14270 0.3518

206 0.12470 0.06213 0.05588 0.2989

65 0.34160 0.30240 0.16140 0.3321

437 0.12520 0.11170 0.07453 0.2725

126 0.28840 0.37960 0.13290 0.3470

429 0.09605 0.03469 0.03612 0.2165

392 0.39130 0.55530 0.21210 0.3187

343 0.34580 0.47340 0.22550 0.4045

334 0.09052 0.03619 0.03983 0.2554

440 0.40820 0.47790 0.15550 0.2540

441 0.41220 0.50360 0.17390 0.2500

137 0.16480 0.13990 0.08476 0.2676

230 0.39340 0.50180 0.25430 0.3109

7 0.36820 0.26780 0.15560 0.3196

408 0.37350 0.33010 0.19740 0.3060

523 0.25660 0.19350 0.12840 0.2849

361 0.16670 0.12120 0.05614 0.2637

553 0.08298 0.07993 0.02564 0.2435

478 0.20100 0.25960 0.07431 0.2941

303 0.10440 0.08423 0.06528 0.2213

.. ... ... ... ...

459 0.11090 0.07190 0.04866 0.2321

510 0.27930 0.26900 0.10560 0.2604

151 0.43100 0.53810 0.07879 0.3322

244 0.29680 0.34580 0.15640 0.2920

543 0.13810 0.10620 0.07958 0.2473

544 0.20370 0.13770 0.06845 0.2249

265 0.26440 0.34420 0.16590 0.2868

288 0.18430 0.15460 0.09314 0.2955

423 0.31670 0.36600 0.14070 0.2744

147 0.25210 0.25000 0.08405 0.2852

177 0.46670 0.58620 0.20350 0.3054

99 0.30260 0.31940 0.15650 0.2718

448 0.26490 0.37790 0.09594 0.2471

431 0.26290 0.24030 0.07370 0.2556

115 0.23990 0.15030 0.07247 0.2438

72 0.73940 0.65660 0.18990 0.3313

537 0.32510 0.13950 0.13080 0.2803

174 0.06791 0.00000 0.00000 0.2710

87 0.32060 0.57550 0.19560 0.3956

551 0.17820 0.15640 0.06413 0.3169

486 0.20700 0.24370 0.07828 0.2455

314 0.07767 0.00000 0.00000 0.3142

396 0.25700 0.34380 0.14530 0.2666

472 0.27910 0.31510 0.11470 0.2688

70 0.23360 0.26870 0.17890 0.2551

277 0.11600 0.22100 0.12940 0.2567

9 1.05800 1.10500 0.22100 0.4366

359 0.10490 0.11440 0.05052 0.2454

192 0.02729 0.00000 0.00000 0.1909

559 0.25170 0.36300 0.09653 0.2112

worst fractal dimension

293 0.07007

332 0.06522

565 0.06637

278 0.06263

489 0.07623

346 0.08083

357 0.07113

355 0.07188

112 0.10820

68 0.11750

526 0.08665

206 0.07380

65 0.08911

437 0.07234

126 0.07900

429 0.06025

392 0.10190

343 0.07918

334 0.07207

440 0.09532

441 0.07944

137 0.06765

230 0.09061

7 0.11510

408 0.08503

523 0.09031

361 0.06658

553 0.07393

478 0.09180

303 0.07842

.. ...

459 0.07211

510 0.09879

151 0.14860

244 0.07614

543 0.06443

544 0.08492

265 0.08218

288 0.07009

423 0.08839

147 0.09218

177 0.09519

99 0.09353

448 0.07463

431 0.09359

115 0.08541

72 0.13390

537 0.09970

174 0.06164

87 0.09288

551 0.08032

486 0.06596

314 0.08116

396 0.07686

472 0.08273

70 0.06589

277 0.05737

9 0.20750

359 0.08136

192 0.06559

559 0.08732

[426 rows x 30 columns],

mean radius mean texture mean perimeter mean area mean smoothness \

512 13.400 20.52 88.64 556.7 0.11060

457 13.210 25.25 84.10 537.9 0.08791

439 14.020 15.66 89.59 606.5 0.07966

298 14.260 18.17 91.22 633.1 0.06576

37 13.030 18.42 82.61 523.8 0.08983

515 11.340 18.61 72.76 391.2 0.10490

382 12.050 22.72 78.75 447.8 0.06935

310 11.700 19.11 74.33 418.7 0.08814

538 7.729 25.49 47.98 178.8 0.08098

345 10.260 14.71 66.20 321.6 0.09882

421 14.690 13.98 98.22 656.1 0.10310

90 14.620 24.02 94.57 662.7 0.08974

412 9.397 21.68 59.75 268.8 0.07969

157 16.840 19.46 108.40 880.2 0.07445

89 14.640 15.24 95.77 651.9 0.11320

172 15.460 11.89 102.50 736.9 0.12570

318 9.042 18.90 60.07 244.5 0.09968

233 20.510 27.81 134.40 1319.0 0.09159

389 19.550 23.21 128.90 1174.0 0.10100

250 20.940 23.56 138.90 1364.0 0.10070

31 11.840 18.70 77.93 440.6 0.11090

283 16.240 18.77 108.80 805.1 0.10660

482 13.470 14.06 87.32 546.3 0.10710

211 11.840 18.94 75.51 428.0 0.08871

372 21.370 15.10 141.30 1386.0 0.10010

401 11.930 10.91 76.14 442.7 0.08872

159 10.900 12.96 68.69 366.8 0.07515

14 13.730 22.61 93.60 578.3 0.11310

364 13.400 16.95 85.48 552.4 0.07937

337 18.770 21.43 122.90 1092.0 0.09116

.. ... ... ... ... ...

500 15.040 16.74 98.73 689.4 0.09883

338 10.050 17.53 64.41 310.8 0.10070

427 10.800 21.98 68.79 359.9 0.08801

406 16.140 14.86 104.30 800.0 0.09495

96 12.180 17.84 77.79 451.1 0.10450

490 12.250 22.44 78.18 466.5 0.08192

384 13.280 13.72 85.79 541.8 0.08363

281 11.740 14.02 74.24 427.3 0.07813

325 12.670 17.30 81.25 489.9 0.10280

190 14.220 23.12 94.37 609.9 0.10750

380 11.270 12.96 73.16 386.3 0.12370

366 20.200 26.83 133.70 1234.0 0.09905

469 11.620 18.18 76.38 408.8 0.11750

225 14.340 13.47 92.51 641.2 0.09906

271 11.290 13.04 72.23 388.0 0.09834

547 10.260 16.58 65.85 320.8 0.08877

550 10.860 21.48 68.51 360.5 0.07431

492 18.010 20.56 118.40 1007.0 0.10010

185 10.080 15.11 63.76 317.5 0.09267

306 13.200 15.82 84.07 537.3 0.08511

208 13.110 22.54 87.02 529.4 0.10020

242 11.300 18.19 73.93 389.4 0.09592

313 11.540 10.72 73.73 409.1 0.08597

542 14.740 25.42 94.70 668.6 0.08275

514 15.050 19.07 97.26 701.9 0.09215

236 23.210 26.97 153.50 1670.0 0.09509

113 10.510 20.19 68.64 334.2 0.11220

527 12.340 12.27 78.94 468.5 0.09003

76 13.530 10.94 87.91 559.2 0.12910

162 19.590 18.15 130.70 1214.0 0.11200

mean compactness mean concavity mean concave points mean symmetry \

512 0.14690 0.144500 0.081720 0.2116

457 0.05205 0.027720 0.020680 0.1619

439 0.05581 0.020870 0.026520 0.1589

298 0.05220 0.024750 0.013740 0.1635

37 0.03766 0.025620 0.029230 0.1467

515 0.08499 0.043020 0.025940 0.1927

382 0.10730 0.079430 0.029780 0.1203

310 0.05253 0.015830 0.011480 0.1936

538 0.04878 0.000000 0.000000 0.1870

345 0.09159 0.035810 0.020370 0.1633

421 0.18360 0.145000 0.063000 0.2086

90 0.08606 0.031020 0.029570 0.1685

412 0.06053 0.037350 0.005128 0.1274

157 0.07223 0.051500 0.027710 0.1844

89 0.13390 0.099660 0.070640 0.2116

172 0.15550 0.203200 0.109700 0.1966

318 0.19720 0.197500 0.049080 0.2330

233 0.10740 0.155400 0.083400 0.1448

389 0.13180 0.185600 0.102100 0.1989

250 0.16060 0.271200 0.131000 0.2205

31 0.15160 0.121800 0.051820 0.2301

283 0.18020 0.194800 0.090520 0.1876

482 0.11550 0.057860 0.052660 0.1779

211 0.06900 0.026690 0.013930 0.1533

372 0.15150 0.193200 0.125500 0.1973

401 0.05242 0.026060 0.017960 0.1601

159 0.03718 0.003090 0.006588 0.1442

14 0.22930 0.212800 0.080250 0.2069

364 0.05696 0.021810 0.014730 0.1650

337 0.14020 0.106000 0.060900 0.1953

.. ... ... ... ...

500 0.13640 0.077210 0.061420 0.1668

338 0.07326 0.025110 0.017750 0.1890

427 0.05743 0.036140 0.014040 0.2016

406 0.08501 0.055000 0.045280 0.1735

96 0.07057 0.024900 0.029410 0.1900

490 0.05200 0.017140 0.012610 0.1544

384 0.08575 0.050770 0.028640 0.1617

281 0.04340 0.022450 0.027630 0.2101

325 0.07664 0.031930 0.021070 0.1707

190 0.24130 0.198100 0.066180 0.2384

380 0.11110 0.079000 0.055500 0.2018

366 0.16690 0.164100 0.126500 0.1875

469 0.14830 0.102000 0.055640 0.1957

225 0.07624 0.057240 0.046030 0.2075

271 0.07608 0.032650 0.027550 0.1769

547 0.08066 0.043580 0.024380 0.1669

550 0.04227 0.000000 0.000000 0.1661

492 0.12890 0.117000 0.077620 0.2116

185 0.04695 0.001597 0.002404 0.1703

306 0.05251 0.001461 0.003261 0.1632

208 0.14830 0.087050 0.051020 0.1850

242 0.13250 0.154800 0.028540 0.2054

313 0.05969 0.013670 0.008907 0.1833

542 0.07214 0.041050 0.030270 0.1840

514 0.08597 0.074860 0.043350 0.1561

236 0.16820 0.195000 0.123700 0.1909

113 0.13030 0.064760 0.030680 0.1922

527 0.06307 0.029580 0.026470 0.1689

76 0.10470 0.068770 0.065560 0.2403

162 0.16660 0.250800 0.128600 0.2027

mean fractal dimension ... worst radius \

512 0.07325 ... 16.410

457 0.05584 ... 14.350

439 0.05586 ... 14.910

298 0.05586 ... 16.220

37 0.05863 ... 13.300

515 0.06211 ... 12.470

382 0.06659 ... 12.570

310 0.06128 ... 12.610

538 0.07285 ... 9.077

345 0.07005 ... 10.880

421 0.07406 ... 16.460

90 0.05866 ... 16.110

412 0.06724 ... 9.965

157 0.05268 ... 18.220

89 0.06346 ... 16.340

172 0.07069 ... 18.790

318 0.08743 ... 10.060

233 0.05592 ... 24.470

389 0.05884 ... 20.820

250 0.05898 ... 25.580

31 0.07799 ... 16.820

283 0.06684 ... 18.550

482 0.06639 ... 14.830

211 0.06057 ... 13.300

372 0.06183 ... 22.690

401 0.05541 ... 13.800

159 0.05743 ... 12.360

14 0.07682 ... 15.030

364 0.05701 ... 14.730

337 0.06083 ... 24.540

.. ... ... ...

500 0.06869 ... 16.760

338 0.06331 ... 11.160

427 0.05977 ... 12.760

406 0.05875 ... 17.710

96 0.06635 ... 12.830

490 0.05976 ... 14.170

384 0.05594 ... 14.240

281 0.06113 ... 13.310

325 0.05984 ... 13.710

190 0.07542 ... 15.740

380 0.06914 ... 12.840

366 0.06020 ... 24.190

469 0.07255 ... 13.360

225 0.05448 ... 16.770

271 0.06270 ... 12.320

547 0.06714 ... 10.830

550 0.05948 ... 11.660

492 0.06077 ... 21.530

185 0.06048 ... 11.870

306 0.05894 ... 14.410

208 0.07310 ... 14.550

242 0.07669 ... 12.580

313 0.06100 ... 12.340

542 0.05680 ... 16.510

514 0.05915 ... 17.580

236 0.06309 ... 31.010

113 0.07782 ... 11.160

527 0.05808 ... 13.610

76 0.06641 ... 14.080

162 0.06082 ... 26.730

worst texture worst perimeter worst area worst smoothness \

512 29.66 113.30 844.4 0.15740

457 34.23 91.29 632.9 0.12890

439 19.31 96.53 688.9 0.10340

298 25.26 105.80 819.7 0.09445

37 22.81 84.46 545.9 0.09701

515 23.03 79.15 478.6 0.14830

382 28.71 87.36 488.4 0.08799

310 26.55 80.92 483.1 0.12230

538 30.92 57.17 248.0 0.12560

345 19.48 70.89 357.1 0.13600

421 18.34 114.10 809.2 0.13120

90 29.11 102.90 803.7 0.11150

412 27.99 66.61 301.0 0.10860

157 28.07 120.30 1032.0 0.08774

89 18.24 109.40 803.6 0.12770

172 17.04 125.00 1102.0 0.15310

318 23.40 68.62 297.1 0.12210

233 37.38 162.70 1872.0 0.12230

389 30.44 142.00 1313.0 0.12510

250 27.00 165.30 2010.0 0.12110

31 28.12 119.40 888.7 0.16370

283 25.09 126.90 1031.0 0.13650

482 18.32 94.94 660.2 0.13930

211 24.99 85.22 546.3 0.12800

372 21.84 152.10 1535.0 0.11920

401 20.14 87.64 589.5 0.13740

159 18.20 78.07 470.0 0.11710

14 32.01 108.80 697.7 0.16510

364 21.70 93.76 663.5 0.12130

337 34.37 161.10 1873.0 0.14980

.. ... ... ... ...

500 20.43 109.70 856.9 0.11350

338 26.84 71.98 384.0 0.14020

427 32.04 83.69 489.5 0.13030

406 19.58 115.90 947.9 0.12060

96 20.92 82.14 495.2 0.11400

490 31.99 92.74 622.9 0.12560

384 17.37 96.59 623.7 0.11660

281 18.26 84.70 533.7 0.10360

325 21.10 88.70 574.4 0.13840

190 37.18 106.40 762.4 0.15330

380 20.53 84.93 476.1 0.16100

366 33.81 160.00 1671.0 0.12780

469 25.40 88.14 528.1 0.17800

225 16.90 110.40 873.2 0.12970

271 16.18 78.27 457.5 0.13580

547 22.04 71.08 357.4 0.14610

550 24.77 74.08 412.3 0.10010

492 26.06 143.40 1426.0 0.13090

185 21.18 75.39 437.0 0.15210

306 20.45 92.00 636.9 0.11280

208 29.16 99.48 639.3 0.13490

242 27.96 87.16 472.9 0.13470

313 12.87 81.23 467.8 0.10920

542 32.29 107.40 826.4 0.10600

514 28.06 113.80 967.0 0.12460

236 34.51 206.00 2944.0 0.14810

113 22.75 72.62 374.4 0.13000

527 19.27 87.22 564.9 0.12920

76 12.49 91.36 605.5 0.14510

162 26.39 174.90 2232.0 0.14380

worst compactness worst concavity worst concave points worst symmetry \

512 0.38560 0.51060 0.20510 0.3585

457 0.10630 0.13900 0.06005 0.2444

439 0.10170 0.06260 0.08216 0.2136

298 0.21670 0.15650 0.07530 0.2636

37 0.04619 0.04833 0.05013 0.1987

515 0.15740 0.16240 0.08542 0.3060

382 0.32140 0.29120 0.10920 0.2191

310 0.10870 0.07915 0.05741 0.3487

538 0.08340 0.00000 0.00000 0.3058

345 0.16360 0.07162 0.04074 0.2434

421 0.36350 0.32190 0.11080 0.2827

90 0.17660 0.09189 0.06946 0.2522

412 0.18870 0.18680 0.02564 0.2376

157 0.17100 0.18820 0.08436 0.2527

89 0.30890 0.26040 0.13970 0.3151

172 0.35830 0.58300 0.18270 0.3216

318 0.37480 0.46090 0.11450 0.3135

233 0.27610 0.41460 0.15630 0.2437

389 0.24140 0.38290 0.18250 0.2576

250 0.31720 0.69910 0.21050 0.3126

31 0.57750 0.69560 0.15460 0.4761

283 0.47060 0.50260 0.17320 0.2770

482 0.24990 0.18480 0.13350 0.3227

211 0.18800 0.14710 0.06913 0.2535

372 0.28400 0.40240 0.19660 0.2730

401 0.15750 0.15140 0.06876 0.2460

159 0.08294 0.01854 0.03953 0.2738

14 0.77250 0.69430 0.22080 0.3596

364 0.16760 0.13640 0.06987 0.2741

337 0.48270 0.46340 0.20480 0.3679

.. ... ... ... ...

500 0.21760 0.18560 0.10180 0.2177

338 0.14020 0.10550 0.06499 0.2894

427 0.16960 0.19270 0.07485 0.2965

406 0.17220 0.23100 0.11290 0.2778

96 0.09358 0.04980 0.05882 0.2227

490 0.18040 0.12300 0.06335 0.3100

384 0.26850 0.28660 0.09173 0.2736

281 0.08500 0.06735 0.08290 0.3101

325 0.12120 0.10200 0.05602 0.2688

190 0.93270 0.84880 0.17720 0.5166

380 0.24290 0.22470 0.13180 0.3343

366 0.34160 0.37030 0.21520 0.3271

469 0.28780 0.31860 0.14160 0.2660

225 0.15250 0.16320 0.10870 0.3062

271 0.15070 0.12750 0.08750 0.2733

547 0.22460 0.17830 0.08333 0.2691

550 0.07348 0.00000 0.00000 0.2458

492 0.23270 0.25440 0.14890 0.3251

185 0.10190 0.00692 0.01042 0.2933

306 0.13460 0.01120 0.02500 0.2651

208 0.44020 0.31620 0.11260 0.4128

242 0.48480 0.74360 0.12180 0.3308

313 0.16260 0.08324 0.04715 0.3390

542 0.13760 0.16110 0.10950 0.2722

514 0.21010 0.28660 0.11200 0.2282

236 0.41260 0.58200 0.25930 0.3103

113 0.20490 0.12950 0.06136 0.2383

527 0.20740 0.17910 0.10700 0.3110

76 0.13790 0.08539 0.07407 0.2710

162 0.38460 0.68100 0.22470 0.3643

worst fractal dimension

512 0.11090

457 0.06788

439 0.06710

298 0.07676

37 0.06169

515 0.06783

382 0.09349

310 0.06958

538 0.09938

345 0.08488

421 0.09208

90 0.07246

412 0.09206

157 0.05972

89 0.08473

172 0.10100

318 0.10550

233 0.08328

389 0.07602

250 0.07849

31 0.14020

283 0.10630

482 0.09326

211 0.07993

372 0.08666

401 0.07262

159 0.07685

14 0.14310

364 0.07582

337 0.09870

.. ...

500 0.08549

338 0.07664

427 0.07662

406 0.07012

96 0.07376

490 0.08203

384 0.07320

281 0.06688

325 0.06888

190 0.14460

380 0.09215

366 0.07632

469 0.09270

225 0.06072

271 0.08022

547 0.09479

550 0.06592

492 0.07625

185 0.07697

306 0.08385

208 0.10760

242 0.12970

313 0.07434

542 0.06956

514 0.06954

236 0.08677

113 0.09026

527 0.07592

76 0.07191

162 0.09223

[143 rows x 30 columns],

293 1

332 1

565 0

278 1

489 0

346 1

357 1

355 1

112 1

68 1

526 1

206 1

65 0

437 1

126 0

429 1

392 0

343 0

334 1

440 1

441 0

137 1

230 0

7 0

408 0

523 1

361 1

553 1

478 1

303 1

459 1

510 1

151 1

244 0

543 1

544 1

265 0

288 1

423 1

147 1

177 0

99 0

448 1

431 1

115 1

72 0

537 1

174 1

87 0

551 1

486 1

314 1

396 1

472 1

70 0

277 0

9 0

359 1

192 1

559 1

Name: target, dtype: int64,

512 0

457 1

439 1

298 1

37 1

515 1

382 1

310 1

538 1

345 1

421 1

90 1

412 1

157 1

89 1

172 0

318 1

233 0

389 0

250 0

31 0

283 0

482 1

211 1

372 0

401 1

159 1

14 0

364 1

337 0

500 1

338 1

427 1

406 1

96 1

490 1

384 1

281 1

325 1

190 0

380 1

366 0

469 1

225 1

271 1

547 1

550 1

492 0

185 1

306 1

208 1

242 1

313 1

542 1

514 0

236 0

113 1

527 1

76 1

162 0

Name: target, dtype: int64)

Question 5

Using KNeighborsClassifier, fit a k-nearest neighbors (knn) classifier with X_train, y_train and using one nearest neighbor (n_neighbors = 1).

This function should return a sklearn.neighbors.classification.KNeighborsClassifier.

from sklearn.neighbors import KNeighborsClassifier

def answer_five():
    X_train, X_test, y_train, y_test = answer_four()
    
    # Your code here
    knn = KNeighborsClassifier(n_neighbors = 1)
    knn.fit(X_train, y_train)
    
    
    return knn
answer_five()

Output:

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',

metric_params=None, n_jobs=1, n_neighbors=1, p=2,

weights='uniform')

Question 6

Using your knn classifier, predict the class label using the mean value for each feature.

Hint: You can use cancerdf.mean()[:-1].values.reshape(1, -1) which gets the mean value for each feature, ignores the target column, and reshapes the data from 1 dimension to 2 (necessary for the precict method of KNeighborsClassifier).

This function should return a numpy array either array([ 0.]) or array([ 1.])

def answer_six():
    cancerdf = answer_one()
    knn = answer_five()
    means = (cancerdf.mean()[:-1].values.reshape(1, -1))
#     print(means.shape)
    # Your code here
    prediction = knn.predict(means)
#     print(prediction)
    ans = np.array(prediction)
    return ans
answer_six()

Output:

array([1])

Question 7

Using your knn classifier, predict the class labels for the test set X_test.

This function should return a numpy array with shape (143,) and values either 0.0 or 1.0.

def answer_seven():
    X_train, X_test, y_train, y_test = answer_four()
    knn = answer_five()
    
    # Your code here
    
    prediction = knn.predict(X_test)
#     print(prediction)
    ans = np.array(prediction)
    return ans
    
#     return # Return your answer
answer_seven()

Output:

(143,)

Question 8

Find the score (mean accuracy) of your knn classifier using X_test and y_test.

This function should return a float between 0 and 1

def answer_eight():
    X_train, X_test, y_train, y_test = answer_four()
    knn = answer_five()
    
    
    # Your code here
    ans = knn.score(X_test, y_test)
    
    return ans
answer_eight()

Out[103]:

0.91608391608391604

Optional plot

Try using the plotting function below to visualize the differet predicition scores between training and test sets, as well as malignant and benign cells.

def accuracy_plot():
    import matplotlib.pyplot as plt

    %matplotlib notebook

    X_train, X_test, y_train, y_test = answer_four()

    # Find the training and testing accuracies by target value (i.e. malignant, benign)
    mal_train_X = X_train[y_train==0]
    mal_train_y = y_train[y_train==0]
    ben_train_X = X_train[y_train==1]
    ben_train_y = y_train[y_train==1]

    mal_test_X = X_test[y_test==0]
    mal_test_y = y_test[y_test==0]
    ben_test_X = X_test[y_test==1]
    ben_test_y = y_test[y_test==1]

    knn = answer_five()

    scores = [knn.score(mal_train_X, mal_train_y), knn.score(ben_train_X, ben_train_y), 
              knn.score(mal_test_X, mal_test_y), knn.score(ben_test_X, ben_test_y)]


    plt.figure()

    # Plot the scores as a bar chart
    bars = plt.bar(np.arange(4), scores, color=['#4c72b0','#4c72b0','#55a868','#55a868'])

    # directly label the score onto the bars
    for bar in bars:
        height = bar.get_height()
        plt.gca().text(bar.get_x() + bar.get_width()/2, height*.90, '{0:.{1}f}'.format(height, 2), 
                     ha='center', color='w', fontsize=11)

    # remove all the ticks (both axes), and tick labels on the Y axis
    plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')

    # remove the frame of the chart
    for spine in plt.gca().spines.values():
        spine.set_visible(False)

    plt.xticks([0,1,2,3], ['Malignant\nTraining', 'Benign\nTraining', 'Malignant\nTest', 'Benign\nTest'], alpha=0.8);
    plt.title('Training and Test Accuracies for Malignant and Benign Cells', alpha=0.8)

# Uncomment the plotting function to see the visualization, 
# Comment out the plotting function when submitting your notebook for grading

accuracy_plot()

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Applied Machine Learning in Python week1 Assignment solution

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