Supervised Learning - Prediction Week 3 Challenge
Question 2 :
Answer :
The differences between L1 and L2 regularization:
L1 regularization penalizes the sum of absolute values of the weights, whereas L2 regularization penalizes the sum of squares of the weights.The differences between L1 and L2 regularization:
L1 regularization penalizes the sum of absolute values of the weights, whereas L2 regularization penalizes the sum of squares of the weights.Question 3:
Answer :
- 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
Question 4:
Answer : This parameter determines how fast or slow we will move towards the optimal weights. If the learning rate is very large we will skip the optimal solution. If it is too small we will need too many iterations to converge to the best values.
Question 6 :
Answer : 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.