Introduction to Machine Learning (Day-7)

Select the metric to measure performance of clustering.

• MSE

• MEA

• Scatter

• Award/Punishment

Which metric computes the magnitude of error to measure performance of regression?

• MEA

• MSE

• RMSE

• All of these

If the accuracy of a non-linear regression (robust to outliers) is to be evaluated, then which is the metric is the best?

• MSE

• MEA

• Precision

• Recall

A regression based model obtained the predicted value of target feature as 2, however the actual value is 3. What is the value of MSE for the above given instance?

• 0

• 1

• 0.5

• None of these

A regression model has obtained MSE=0, then which of the following is/are false?

• The model may be overfitted.

• The model may be underfitted.

• The model gives zero training error.

• 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

The confusion matrix is used to:

• Understand how attributes are related to each other

• How data is spread on each dimension

• Evaluate performance of classification algorithms within each class

• it is a visual representation of the actual distribution of predicted values of target labels in context of the actual values of the target labels

The value of precision of a model is 0.5, and it has true positives=60, then what is the value of false positives?

• 50

• 55

• 60

• 65

The value of recall of a model is 0.5, and it has true positives=60, then what is the value of false negatives?

• 50

• 55

• 60

• 65

Which of the following is/are false?

• Precision quantifies the number of positive class predictions that actually belong to the positive class.

• Recall quantifies the number of positive class predictions made out of all positive examples in the dataset.

• Accuracy indicates fraction of positive classes among total detected classes.

• None of these