Think Different

  Feature scaling is the process of normalizing the range of features in a dataset. This is done to ensure that all features are on a similar scale and that no single feature dominates the model. There are two main reasons why feature scaling is done in machine learning: To improve the performance of machine learning algorithms. To make the interpretation of machine learning models easier. Improving the performance of machine learning algorithms Machine learning algorithms often work better when the features are on a similar scale. This is because some machine learning algorithms are sensitive to the scale of the features. For example, a linear regression model will be more accurate if the features are all on a similar scale. Making the interpretation of machine learning models easier Feature scaling can also make the interpretation of machine learning models easier. This is because it can help to identify the most important features in the model. For example, if a feature is ...