State True or False: Standardization of features is not required before training a Logistic regression model True False

The statement "Standardization of features is not required before training a Logistic regression model" is False.

Explanation:

1. Nature of Logistic Regression: Logistic regression is sensitive to the scale of the features. If the features are on different scales, the optimization algorithm may converge slowly or not at all.

2. Importance of Standardization: Standardizing features (i.e., scaling them to have a mean of 0 and a standard deviation of 1) ensures that each feature contributes equally to the result. This can help improve the performance of the model, as it balances the impact of each feature.

3. Recommended Practice: While it may not strictly be required, it is generally recommended to standardize features before fitting a logistic regression model, particularly when features are on different scales or units.

In conclusion, it is beneficial to standardize features before training a logistic regression model, making the statement false.