To predict the sales value of a house, an analyst gathered data about the houses sold in the neighborhood. One of the variables in the dataset is construction material. The options for construction material are stucco, concrete, cement composites, and brick. To properly model this variable in a regression model, how many dummy variables should the analyst create?

Backward elimination is:

Which of the following statement is true about k-NN algorithm?i.k-NN performs much better if all of the data have the same scaleii.it is a model driven approachiii.k-NN makes no assumptions about the functional form of the problem being solved

In binary logistic regression:

In binary logistic regression:

How to select best parameters in tree based models?

In k-NN what will happen when you increase/decrease the value of k?

Which of the following is a real world application of the SVM?

Which of the following is NOT an assumption of linear regression?

Which is the larger measure of distance, the Euclidean distance or the City Block one?