Supervised Learning - Prediction Week 3 Challenge

ANS . 2 Difference between L1 & L2 Gradient descent method:

L1 regularization adds the absolute values of the coefficients to the cost function, promoting sparsity.

L2 regularization adds the squared values of the coefficients, preventing large weights and encouraging small, distributed weights.

ANS.3 Different loss functions for regression:

Mean Squared Error (MSE)

Mean Absolute Error (MAE)

Huber Loss

Log-Cosh Loss

ANS.4- Importance of learning rate:

Learning rate controls the step size in the gradient descent algorithm.

Too small a learning rate may result in slow convergence, while too large a rate can cause overshooting.

It's crucial to find an optimal learning rate for faster convergence and stable training.

ANS 5 .

How to evaluate linear regression:

Common metrics include Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared.

Split the data into training and testing sets to evaluate the model's performance on unseen data.

aNS 6.

Difference between multiple and adjusted coefficient of determination:

The multiple coefficient of determination (R-squared) measures the proportion of variance in the dependent variable explained by all independent variables.

The adjusted R-squared adjusts for the number of predictors, penalizing the addition of irrelevant predictors.