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Applied Machine Learning in Python Module 3 Quiz Answer

by Niyander

Hello Friends in this article i am gone to share Applied Machine Learning in Python Coursera Module 3 Quiz Answers with you..

Applied Machine Learning in Python Quiz Answer

Also visit this link:ย  Applied Machine Learning in Python Module 2 Quiz Answer

 

Question 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

 

Question 2) Given the following confusion matrix:

Applied Machine Learning in Python Module 3 Quiz Answer

Compute the accuracy to three decimal places.

  • 0.906
ย 

Question 3) Given the following confusion matrix:

Applied Machine Learning in Python Module 3 Quiz Answer
Compute the precision to three decimal places.ย 
  • 0.923ย 
ย 

Question 4) Given the following confusion matrix:

Applied Machine Learning in Python Module 3 Quiz Answer
Compute the precision to three decimal places.ย 
  • 0.960

Question 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 recallย 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()` )
  1. #print(m)
  2. pre,rec,_ = precision_recall_curve(y_test,m.predict(X_test))
  3. plt.plot(rec,pre)
  4. plt.xlabel(‘Recall’)
  5. plt.ylabel(‘Precision’)
  6. plt.ylim([0.0, 1.05])
  7. plt.xlim([0.0, 1.0])
  8. plt.show()
ย 
  • 0.6

Question 6)Given the following models and AUC scores, match each model to itsย corresponding ROC curve.

โ€ข Model 1 test set AUC score: 0.91
โ€ข Model 2 test set AUC score: 0.50
โ€ข Model 3 test set AUC score: 0.56
Applied Machine Learning in Python Module 3 Quiz Answer

ย 

โ€ข Model 1: Roc 1
โ€ข Model 2: Roc 2
โ€ข Model 3: Roc 3
โ€ข Model 1: Roc 1ย 
โ€ข Model 2: Roc 3
โ€ข Model 3: Roc 2
โ€ข Model 1: Roc 2
โ€ข Model 2: Roc 3
โ€ข Model 3: Roc 1
โ€ข Model 1: Roc 3
โ€ข Model 2: Roc 2
โ€ข Model 3: Roc 1
ย 
โ€ขย Not enough information is given.ย 
ย 
ย 
Question 7) Given the following models and accuracy scores, match each model to its corresponding ROC curve.
ย 
โ€ข Model 1 test set accuracy: 0.91
โ€ข Model 2 test set accuracy: 0.79
โ€ข Model 3 test set accuracy: 0.72
ย 
Applied Machine Learning in Python Module 3 Quiz Answer

 

โ€ข Model 1: Roc 1
โ€ข Model 2: Roc 2
โ€ข Model 3: Roc 3
โ€ข Model 1: Roc 1
โ€ข Model 2: Roc 3
โ€ข Model 3: Roc 2
โ€ข Model 1: Roc 2
โ€ข Model 2: Roc 3
โ€ข Model 3: Roc 1
โ€ข Model 1: Roc 3
โ€ข Model 2: Roc 2
โ€ข Model 3: Roc 1

 

โ€ข Not enough information is given.

 

Question 8)
Using the fitted model `m` what is the micro precision score?
(Use y_test and X_test to compute the precision score.)
  1. #print(m)
  2. print(precision_score(y_test,m.predict(X_test),average=’micro’))
  • 0.744
Question 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
Question 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
Question 11)
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
Question 12)
A classifier is trained on an imbalanced multiclass dataset. After looking at the modelโ€™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.
Question 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)
ย 
(Use y_test and X_test to compute precision and recall.)
  1. #print(m)ย 
  2. parameters = {‘gamma’:[0.01, 0.1, 1, 10], ‘C’:[0.01, 0.1, 1, 10]}ย 
  3. clf = GridSearchCV(m,parameters,scoring=’recall’)ย 
  4. clf.fit(X_train,y_train)ย 
  5. y_pred = clf.best_estimator_.predict(X_test)ย 
  6. rec = recall_score(y_test, y_pred, average=’binary’)ย 
  7. pre = precision_score(y_test, y_pred, average=’binary’)ย 
  8. print(rec-pre)

 

  • 0.52ย 
ย 
Question 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.)
  1. #print(m)
  2. parameters = {‘gamma’:[0.01, 0.1, 1, 10], ‘C’:[0.01, 0.1, 1, 10]}
  3. clf = GridSearchCV(m,parameters,scoring=’precision’)
  4. clf.fit(X_train,y_train)
  5. y_pred = clf.best_estimator_.predict(X_test)
  6. rec = recall_score(y_test, y_pred, average=’binary’)
  7. pre = precision_score(y_test, y_pred, average=’binary’)
  8. print(pre-rec)
ย 
  • 0.15