Introduction to Machine Learning (Day-8)

Suppose, you designed a model to predict chances of a rain on a particular day. Which metric will you use to measure accuracy of the model?

• F-1 score

• Preceision

• Recall

• Mean square error

Suppose, you designed a model to predict grades of a student in a subject. Which metric will you use to measure accuracy of the model?

• F-1 score

• Preceision

• Recall

• Mean square error

Adding a non-important feature to a linear regression model may result in.

1.Increase in MSE

2.Decrease in R-square

• 1

• 2

• 1 and 2

• none of these

Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data.

Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case?

1. Accuracy metric is not a good idea for imbalanced class problems.
2. Accuracy metric is a good idea for imbalanced class problems.
3. Precision and recall metrics are good for imbalanced class problems.
4. Precision and recall metrics aren’t good for imbalanced class problems.

• 1 and 3

• 1 and 4

• 2 and 3

• 2 and 4

What is meant by the term 'accuracy'?

• The lack of bias in the data.

• The level of detail at which data is stored.

• The extent to which a value approaches its true value.

• The overall quality of the data.

What will be the value of F-1 score, if preceision=recall=0.5?

• 1

• 0.5

• 2

• None of these