The CART model of decision tree, by default, uses "Gini" index for selecting variable for node splitting.

The store manager at Navi Mumbai IKEA stores is building a machine learning model to predict probability of next order by a customer using the number of past store visits and income as the two predictors. From the exploratory analysis, the manager knows that these two variables are independent of each other and normally distributed.  Which model do you recommend for him?

Which of the following is a dataset frequently available in supervised learning tasks?

Assume that you have dataset size of 100 datapoints. You plan to apply 10-fold cross-validation for evaluating a decision tree model with max_depth=5. How many datapoints will be there in each fold and how many test folds will be used during the cross-validation?

Which of the following statements is incorrect on cross-validation?

Which of the following is not correct about K-means clustering?

Which ensemble technique is prone to overfitting?

Study the two given confusion matrices (CM) given below. M1 is the CM for the KNN model and M2 is the CM for a Naive Bayes model. Both models are built using the same train-test datasets and are used for predicting loan default. Choose the correct option from the given alternatives.

Linear regression and Linear Discriminant Analysis, both are suitable for numeric outcome prediction.

Consider the following confusion matrix and choose the correct option.