UNIT 2 - ML

1) When you have output variable as categorical variable then you make use of ___________algorithm.

• Regression

• Classification

• Unsupervised

• Reinforcement

2) For prediction of continuous variables we make use of ______________ algorithms.

• Regression

• Classification

• Deep Learning

• Hybrid

3) Multicollinearity will create problem in ________.

• While having dependent variable.

• While ranking the least affecting variable.

• While ranking the most affecting variable.

• While least dependent variable.

4) If more than one independent variable is used to predict the value of a numerical dependent variable then it is called as ______________.

• Simple Linear regression

• Logistic Regression

• Decision Tree Regression

• Multiple Linear Regression

5) Equal variance in residuals is called as _____________

• Homoscedasticity

• Heteroscedasticity

• Autocorrelation

• Multicollinearity

6) Under classification which of the following is a linear models_____

• KNN Classifier

• Naive Bayes Classifier

• Logistic Regression

• Random Forest

7) The best decision boundary in Support Vector Machine is called____________.

• Best fit line

• Hyperplane

• Regression line

• Straight line

8) Which of the following algorithm mimic human thinking while making a decision?

• Naive Bayes

• Support Vector Machine

• Random Forest

• Decision Tree

9) K-Nearest Neighbour is a _____________algorithm.

• Unsupervised

• Parametric

• Non parametric

• Neighboring

10) Overfitting problem can be dealt with the help of a ________technique.

• Jupyter Notebook

• Classification

• Graphs

• Regularization