Applied Machine Learning in Python week3 quiz answers course era
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
answers are in green colour
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1。
A supervised learning model has been built to predict whether someone is
infected with a new strain of a virus. The probability of any one person having the
virus is 1%. Using accuracy as a metric, what would be a good choice for a
baseline accuracy score that the new model would want to outperform?
0.99
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2。
Given the following confusion matrix:
Predicted Positive PredicteNegative
Condition Positive 96 4
ConditionNegative 8 19
Compute the accuracy to three decimal places.
0.906
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3。
Given the following confusion matrix:
Predicted Positive PredicteNegative
Condition Positive 96 4
ConditionNegative 8 19
Compute the precision to three decimal places.
0.923
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4。
Given the following confusion matrix:
Predicted Positive PredicteNegative
Condition Positive 96 4
ConditionNegative 8 19
Compute the recall to three decimal places.
0.960
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5。
Using the fitted model `m` create a precision-recall curve to answer the following
question:
For the fitted model `m`, approximately what precision can we expect for a recall
of 0.8?
(Use y_test and X_test to compute the precision-recall curve. If you wish to view a
plot, you can use `plt.show()` )
0.6
#print(m)pre,rec,_ = precision_recall_curve(y_test,m.predict(X_test))plt.plot(rec,pre)plt.xlabel('Recall')plt.ylabel('Precision')plt.ylim([0.0, 1.05])plt.xlim([0.0, 1.0])plt.show()
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6。
Given the following models and AUC scores, match each model to its
corresponding ROC curve.
• Model 1 test set AUC score: 0.91
• Model 2 test set AUC score: 0.50
• Model 3 test set AUC score: 0.56
• Model 1: Roc 1
• Model 2: Roc 2
• Model 3: Roc 3
• Model 1: Roc 1
• Model 2: Roc 3
• Model 3: Roc 2
• Model 1: Roc 2
• Model 2: Roc 3
• Model 3: Roc 1
• Model 1: Roc 3
• Model 2: Roc 2
• Model 3: Roc 1
Not enough information is given.
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7。
Given the following models and accuracy scores, match each model to its
corresponding ROC curve.
• Model 1 test set accuracy: 0.91
• Model 2 test set accuracy: 0.79
• Model 3 test set accuracy: 0.72
• Model 1: Roc 1
• Model 2: Roc 2
• Model 3: Roc 3
• Model 1: Roc 1
• Model 2: Roc 3
• Model 3: Roc 2
• Model 1: Roc 2
• Model 2: Roc 3
• Model 3: Roc 1
• Model 1: Roc 3
• Model 2: Roc 2
• Model 3: Roc 1
Not enough information is given.
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Using the fitted model `m` what is the micro precision score?
8。(Use y_test and X_test to compute the precision score.)
0.744
-------------------------------------------------------------------------------------#print(m)print(precision_score(y_test,m.predict(X_test),average='micro'))
9。
Which of the following is true of the R-Squared metric? (Select all that apply)
The best possible score is 1.0
A model that always predicts the mean of y would get a negative score
A model that always predicts the mean of y would get a score of 0.0
The worst possible score is 0.0
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10。
In a future society, a machine is used to predict a crime before it occurs. If you
were responsible for tuning this machine, what evaluation metric would you want
to maximize to ensure no innocent people (people not about to commit a crime)
are imprisoned (where crime is the positive label)?
Accuracy
Precision
Recall
F1
AUC
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11。
Consider the machine from the previous question. If you were responsible for
tuning this machine, what evaluation metric would you want to maximize to
ensure all criminals (people about to commit a crime) are imprisoned (where
crime is the positive label)?
Accuracy
Precision
Recall
F1
AUC
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12。
A classifier is trained on an imbalanced multiclass dataset. After looking at the
model’s precision scores, you find that the micro averaging is much smaller than
the macro averaging score. Which of the following is most likely happening?
The model is probably misclassifying the frequent labels more than the
infrequent labels.
The model is probably misclassifying the infrequent labels more than
the frequent labels.
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13。
Using the already defined RBF SVC model `m`, run a grid search on the
parameters C and gamma, for values [0.01, 0.1, 1, 10]. The grid search should find
the model that best optimizes for recall. How much better is the recall of this
model than the precision? (Compute recall - precision to 3 decimal places)
(Use y_test and X_test to compute precision and recall.)
0.52
#print(m)parameters = {'gamma':[0.01, 0.1, 1, 10], 'C':[0.01, 0.1, 1, 10]}clf = GridSearchCV(m,parameters,scoring='recall')clf.fit(X_train,y_train)y_pred = clf.best_estimator_.predict(X_test)rec = recall_score(y_test, y_pred, average='binary')pre = precision_score(y_test, y_pred, average='binary')print(rec-pre)
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14。
Using the already defined RBF SVC model `m`, run a grid search on the
parameters C and gamma, for values [0.01, 0.1, 1, 10]. The grid search should find
the model that best optimizes for precision. How much better is the precision of
this model than the recall? (Compute precision - recall to 3 decimal places)
(Use y_test and X_test to compute precision and recall.)
0.15
#print(m)parameters = {'gamma':[0.01, 0.1, 1, 10], 'C':[0.01, 0.1, 1, 10]}clf = GridSearchCV(m,parameters,scoring='precision')clf.fit(X_train,y_train)y_pred = clf.best_estimator_.predict(X_test)rec = recall_score(y_test, y_pred, average='binary')pre = precision_score(y_test, y_pred, average='binary')print(pre-rec)
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