State True or False: Standardization of features is not required before training a Logistic regression model Linear Regression errors values have to be normally distributed but in the case of Logistic Regression it is not the case Logistic Regression errors values have to be normally distributed but in the case of Linear Regression it is not the case Both Linear Regression and Logistic Regression error values have to be normally distributed Both Linear Regression and Logistic Regression error values have not to be normally distributed

The statement is False.

1. Standardization of features can be beneficial before training a Logistic regression model, especially when features are on different scales. It is not strictly required, but it can improve model performance and interpretability.

2. Linear Regression assumes that errors are normally distributed. This is one of the key assumptions of Linear Regression.

3. Logistic Regression does not require that errors are normally distributed. This is because Logistic Regression is a classification algorithm, and the concept of distribution of errors doesn't apply in the same way as it does for Linear Regression.

4. Both Linear Regression and Logistic Regression do not require error values to be normally distributed. As mentioned above, this is a requirement for Linear Regression, but not for Logistic Regression.

5. Both Linear Regression and Logistic Regression do not require error values to be normally distributed. This statement is partially true. Linear Regression does require normally distributed error values, but Logistic Regression does not.