What is feature scaling and why is it important in machine learning?

What is feature scaling and why is it important in machine learning?

Feature scaling is the process of normalizing or standardizing the range of independent variables in data. It's important because it ensures that each feature contributes equally to the distance calculations in algorithms like KNN or gradient descent.

Can you explain the different methods of feature scaling?

Sure! The two most common methods are Min-Max scaling, which rescales the feature to a fixed range, usually 0 to 1, and Standardization, which centers the feature around zero with a standard deviation of one.

When should I use Min-Max scaling over Standardization?

Use Min-Max scaling when your data is bounded and you want to preserve the relationships between the features. Standardization is better when your data follows a Gaussian distribution.

Are there any drawbacks to feature scaling?

Yes, feature scaling can sometimes lead to loss of information, especially if the original distribution of the data is important for the model's performance.