Feature Scaling

Techniques

Standard Scaler standardizes data by subtracting the mean and dividing by the standard deviation. This is generally preferred for machine learning models as it:

• Makes all features have zero mean and unit variance, which can improve model performance.

• Ensures all features contribute equally to the model, regardless of their original units or scales.

Min-Max Scaler scales the data to a specific range, typically between 0 and 1. This may be useful in certain cases, but it can be problematic for machine learning models because:

• It removes information about the spread of the data (variance), which can be important for certain models.

• It can amplify the effect of outliers.