K-Nearest Neighbors (kNN)

Which of the following option is true about k-NN algorithm?

• It can be used for classification

• it can be used for regression

• It can be used in both classification and regression

Which of the following machine learning algorithm can be used for imputing missing values of both categorical and continuous variables?

• Linear Regression

• K-NN

• Logistic Regression

Which of the following statement is true about k-NN algorithm?

1- k-NN performs much better if all of the data have the same scale.

2-k-NN works well with a small number of features (X's), but struggles when the number of inputs is very large

3-k-NN makes no assumptions about the functional form of the problem being solved

• 1 and 2

• 1 and 3

• Only 1

• All of the above

When you find noise in data which of the following option would you consider in k-NN?

• I will increase the value of k

• I will decrease the value of k

• Noise can not be dependent on value of k

• None of these

Which of the following distance measure do we use in case of categorical variables in k-NN?

1.Hamming Distance

2.Euclidean Distance

3.Manhattan Distance

• 1

• 2

• 3

• 1 & 2

• 2&3