Supervised Learning - Prediction Week 3 Challenge

ques 2
What is the difference between L1 and L2 loss?
L2 Loss function. Least absolute deviations(L1) and Least square errors(L2) are the two standard loss functions,
that decides what function should be minimized while learning from a dataset. On the other hand,
L2 loss function minimizes the squared differences between the estimated and existing target values.

ques 3
Loss functions used in Regression:
Mean Squared Error/L2 Loss: This is the most used loss function as it is very easy to understand and implement. ...
Mean Absolute Error / L1 Loss
Root Mean Squared Error
Mean Bias Error
Huber Loss

ques 4

The learning rate controls how quickly the model
is adapted to the problem. Smaller learning rates
require more training epochs given the smaller
changes made to the weights each update,
whereas larger learning rates result in rapid changes and
require fewer training epochs

ques 5

There are 3 main metrics for model evaluation in regression:
1. R Square/Adjusted R Square

2. Mean Square Error(MSE)/Root Mean Square Error(RMSE)

3. Mean Absolute Error(MAE)

ques 6

The fundamental point is that when you add predictors to your model,
the multiple Rsquared will always increase,
as a predictor will always explain some portion of the variance.
Adjusted Rsquared controls against this increase,
and adds penalties for the number of predictors in the model

********hope everything is correct if any mistakes please mention in remarks************