ML-QUIZ

1. What does multicollinearity refer to in the context of multiple linear regression?

• • A) When predictor variables are highly correlated with each other

• B) When the response variable is not normally distributed

• • C) When residuals are not homoscedastic

• • D) When the model's intercept is zero

2. Which of the following metrics is commonly used to evaluate the performance of a linear regression model?

• • A) Accuracy

• • B) Precision

• • C) Mean Squared Error (MSE)

• • D) F1 Score

3. What is the main drawback of using a linear regression model with too many predictors?

• • A) The model will always be biased

• • B) The model may suffer from multicollinearity and overfitting

• • C) The model will have a lower R-squared value

• • D) The model will be less interpretable

4. The target variable is represented along ____________
a) Y axis
b) X axis
c) Either Y-axis or X-axis, it doesn’t matter
d) Depends on the dataset

5. Hypothesis h maps from x (independent variable) to y (dependent variable).Learning algorithm outputs the hypothesis.
a) False
b) True

6. Which of the following is NOT a step in performing PCA?

• • A) Standardizing the data

• • B) Computing the covariance matrix

• • C) Selecting the optimal number of clusters

• • D) Performing eigen decomposition on the covariance matrix

7. In PCA, what is the purpose of the covariance matrix?

• • A) To compute the distance between data points

• • B) To measure the variance and covariance between features

• • C) To calculate the eigenvalues of the data

• • D) To cluster data points into groups

8. If the first principal component explains 70% of the variance in the data, what does this imply about the remaining principal components?

• • A) They explain 30% of the variance collectively

• • B) They explain less variance than the first component individually

• • C) They explain 70% of the variance collectively

• • D) They explain 100% of the variance collectively

9. Which of the following is NOT typically a use case for PCA?

• • A) Data visualization

• • B) Feature reduction

• • C) Noise reduction

• • D) Predicting categorical outcomes

10. Dimensionality reduction can help in addressing which of the following issues?

• • A) High variance in the data

• • B) Overfitting due to having too many features

• • C) Missing values in the dataset

• • D) Non-linearity in the data