Supervised Learning - Prediction Week 3 Challenge

Ans 2. L1 regularization penalizes the sum of absolute values of the weights, whereas L2 regularization

penalizes the sum of squares of the weights.

The L1 regularization solution is sparse. The L2 regularization solution is non-sparse. L2 regularization doesn’t perform feature selection, since weights are only reduced to values near 0

instead of 0. L1 regularization has built-in feature selection.

L1 regularization is robust to outliers, L2 regularization is not.

Ans 3. We use different types of regression methods.

a) MSE (L2 Loss function)

b) MAE (Mean Absolute Error)

c) Root Mean Squared Error

Ans 4. Learning rate in the Gradient descent defines the rate at which the tangent line to be drawn or in simple terms, the step in each tangent line. More the learning rate, the step will be high, which skips the local minima and doesn't give proper solution. If the learning rate is too small, the accuracy will be high but the computational cost will be high. So, optimal learning rate to be chosed before solving a Linear Regression problem.

Ans 5. To evaluate the Regression problem, we have two techniques,

a) Coefficient of Determination (R-squared)

b) Mean Squared Error (MSE)

c) Mean Absolute Error (MAE)

d) Root Mean Squared Error (RMSE)

e) Mean Absolute Percentage Error (MAPE)