Practice Quiz Module 5

The true positive value is 10 and the false positive value is 15. Calculate the value of precision- 0.4 0.6 0.5 None

0.4

If the precision is 0.6 and the recall value is 0.4. What will be the f measure? 0.6 0.3 0.5 0.4

0.5

The false negative value is 5 and the true positive value is 20. What will be the value of recall- 0.8 0.2 0.6 0.3

0.8

According to the ROC curve and AUC below, which answer is correct in terms of an effective classifier? None of the above A < B A > B A = B

A > B

What is the definition of the ROC curve? A plot of precision versus sensitivity (recall) A plot of accuracy versus sensitivity (recall) A plot of accuracy versus specificity A plot of sensitivity (recall) versus 1 - specificity

A plot of sensitivity (recall) versus 1 - specificity

Which of the following metrics are used to evaluate classification models? Area under the ROC curve Confusion matrix All of the above F1 score

All of the above

With the help of a confusion matrix, we can compute All of the above Accuracy Precision Recall

All of the above

False negatives are- Predicted negatives that are actually positives Predicted positives that are actually positives Predicted negatives that are actually negatives Predicted positives that are actually negatives

Predicted negatives that are actually positives

Which of the following classifiers are capable of handling multiple classes natively. SGD classifier Support Vector Machine classifiers Logistic Regression

SGD classifier

Classification is- Reinforcement learning None Supervised learning Unsupervised learning

Supervised learning

A classifier- Inputs a vector of discrete values and outputs a single discrete value Inputs a vector of continuous values and outputs a single discrete value Both A and B None

Both A and B

You have a dataset of different flowers containing their petal lengths and color. Your model has to predict the type of flower for given petal lengths and color. This is a Regression task Classification task Clustering task None

Classification task

A completely effective classifier is a diagonal line, and it will have an AUC of 0.5. True Correct answer: False

False

Accuracy is a preferred performance measure for classifiers, especially when you are dealing with skewed datasets (some classes are much more frequent than others).

False

Suppose your classification model predicted true for a class which actual value was false. Then this is a- True positive False negative True negative False positive

False positive

Which metric is not used for evaluating classification models? Precision AUC ROC score Accuracy Mean absolute error

Mean absolute error

Which of the following statements are true? Multi-label classification problems have each instance can be assigned with multiple categories or a set of target labels. Multi-class classification problems have multiple categories but each instance is assigned only once.

Multi-label classification problems have each instance can be assigned with multiple categories or a set of target labels. Multi-class classification problems have multiple categories but each instance is assigned only once.

Which of the following strategies can be used to perform multi-class classification with multiple binary classifiers? One-versus-the-rest One-versus-one One-versus-all None of the above

One-versus-the-rest One-versus-one One-versus-all

Which of the following is a perfect metric to evaluate spam classification task? Recall Precision F1-score Accuracy

Precision

F1 score is the harmonic mean of precision and recall.

True

Classification is appropriate when you- Both A and B for different contexts Try to predict a continuous valued output Try to predict a class or discrete output None

Try to predict a class or discrete output