Supervised Learning - Prediction Week 3 Challenge

1.)

2.)

L1 Gradient Descent Method	L2 gradient descent method
MAE (MEAN ABSOLUTE ERROR)	MSE (MEAN SQUARED ERROR)

3.) Different Loss Functions for Regression are:

1. Mean Absolute Error (MAE)
2. Mean Squared Error (MSE)
3. Root Mean Squared Error (RMSE)
4. Mean Absolute Percentage Error (MAPE)
5. Huber Loss
6. Log-Cosh Loss

4.) Learning reate is a Tuning Parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. Learning Rate is used to scale the magnitude of parameter updates during gradient descent. The choice of the value for learning rate can be impact two things: 1) How fast the algorithm learns and 2) whether the cost function is minimised or not.

5.) There are 3 main metrics for model evaluation in regression. they are

1. R squared
2. Mean Squared Error (MSE)
3. Mean Absolute Error (MAE)

6.) Difference between Multiple and adjusted coefficient of determination

Multiple Coefficient of Determination	Adjusted Coefficient of Determination
R squared or coefficient of determination is the proportion of the variance in the dependent variable that is predicted from independent variable.	We can Use Adjusted Coefficient of Determination to determine how well a multiple regression equation "fits" the sample data. Adjusted R2 alsways takes on a value between 0 and 1. the closer adjusted R2 is to 1, the better the estimated regression equation fits.