Adding a new feature to the model always results in equal or better performance on the training set?

• True

• False

Which of these isn’t a classification type?

• Binary Classification

• Multi-class multilabel

• Single class multilabel

• Multi-class Single Label

Which of these is not a supervised learning algorithm?

• Representation Learning

• Classification

• Regression

Which of these metrics are used to evaluate classification algorithm?

• F1 Score

• AUC

• Predicted vs True Chart

• Precision

_____ is a learning model that is used to identify a relationship between large amounts of information from a data set.

• Classification

• Association

• Unsupervised Learning

• Multiclass Classification

A computer program is said to learn from experience E with respect to some task T and some performance measure P if its performance on T, as measured by P, improves with experience E. Suppose we feed a learning algorithm a lot of historical weather data, and have it learn to predict weather. In this setting, what is E?

• Historical weather data.

• The process of the algorithm examining a large amount of historical weather data.

• Learning Rate

• Performance measure

What should be the ideal split percentage for data in machine learning?

• training data (70%), test data (10%), validation data (20%)

• training data (50%), test data (20%), validation data (30%)

• test data (70%), training data (10%), validation data (20%)

• test data (20%), training data (70%), validation data (10%)

Which of the following options is/are true for K-fold cross-validation?

• Increase in K will result in higher time required to cross validate the result.

• Higher values of K will result in higher confidence on the cross-validation result as compared to lower value of K.

• If K=N, then it is called Leave one out cross validation, where N is the number of observations.

Which of the following option is true for overall execution time for 5-fold cross validation with 10 different values of “max_depth”?

• Less than 100 seconds

• 100 – 300 seconds

• Can’t estimate

• More than or equal to 600 seconds

• None of the above

Imagine you are working on a project which is a binary classification problem. You trained a model on training dataset and get the below confusion matrix on validation dataset.

Based on the above confusion matrix, choose which option(s) below will give you correct predictions?

• Accuracy is ~0.91

• Misclassification rate is ~ 0.91

• False positive rate is ~0.95

• True positive rate is ~0.95