What is feature scaling and why is it important in data preprocessing?

What is feature scaling and why is it important in data preprocessing?

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, especially those that rely on distance metrics like k-NN or gradient descent.

What are the common methods of feature scaling?

The common methods include Min-Max scaling, which rescales features to a range of [0, 1], and Standardization, which centers the data around the mean with a unit standard deviation.

When should I use Min-Max scaling over Standardization?

Use Min-Max scaling when you want to preserve the relationships in the data and when the data is not normally distributed. Standardization is preferable when the data follows a Gaussian distribution.

Can feature scaling affect the performance of my model?

Yes, feature scaling can significantly impact model performance, especially for algorithms sensitive to the scale of data. Proper scaling can lead to faster convergence and better accuracy.