Question 1

What does Linear Regression aim to model?

The relationship between dependent and independent variables The variance of data points Non-linear patterns in data The classification of data

Question 2

What is the primary objective of Linear Regression?

To maximize the slope of the line To minimize the Mean Squared Error (MSE) To increase the number of data points To classify data into categories

Question 3

What is a loss function in machine learning?

A function that measures the discrepancy between predicted and actual values A function used to calculate gradients A function that normalizes data A function that splits the dataset into train and test sets

Question 4

Which loss function is most sensitive to outliers?

Mean Squared Error (MSE) Mean Absolute Error (MAE) Cross-Entropy Loss Huber Loss

Question 5

Which loss function is commonly used for classification problems?

Mean Squared Error (MSE) Cross-Entropy Loss Mean Absolute Error (MAE) Hinge Loss

Question 6

What is the primary goal of Gradient Descent?

To find the maximum value of a function To minimize the cost function To calculate the slope of a line To increase the dataset size

Question 7

What does the learning rate control in Gradient Descent?

The speed of convergence The size of the dataset The type of loss function used The number of iterations

Question 8

What happens if the learning rate is too high in Gradient Descent?

The algorithm converges faster The algorithm may overshoot the minimum The algorithm stops prematurely The algorithm runs indefinitely

Question 9

What is the role of the gradient in Gradient Descent?

To point towards the steepest ascent To point towards the steepest descent To normalize the data To calculate the slope of a line

Question 10

Which parameter of Gradient Descent affects the step size?

Cost function Learning rate Batch size Number of features

Question 11

What is Mean Squared Error (MSE) used for?

Measuring accuracy in classification tasks Measuring variance in clustering Measuring error in regression tasks Scaling features in data preprocessing

Question 12

Which loss function increases linearly with the error?

Mean Squared Error (MSE) Mean Absolute Error (MAE) Cross-Entropy Loss Log Loss

Question 13

What is the optimal condition for Gradient Descent to stop?

When the gradient approaches zero When the cost function reaches its maximum When the number of iterations is exceeded When the dataset is normalized

Question 14

Which visualization helps understand the Gradient Descent process?

Scatter plot Cost function curve Box plot Confusion matrix

Question 15

What is the key difference between MSE and MAE?

MSE penalizes larger errors more than MAE MSE is used for classification tasks, and MAE is used for regression MSE is less sensitive to outliers compared to MAE MSE is faster to compute than MAE

Linear Models and Optimization