Feature scaling is commonly used to improve the performance and stability of ML models. (View Highlight)
This is because it scales the data to a standard range. This prevents a specific feature from having a strong influence on the model’s output. (View Highlight)
For instance, in the image above, the scale of income could massively impact the overall prediction. Scaling both features to the same range can mitigate this and improve the model’s performance. (View Highlight)
In my opinion, while feature scaling is often crucial, we often overlook whether it is even needed or not. (View Highlight)
But as counterintuitive as it may sound, the first step towards imputing missing data should NEVER be imputation. (View Highlight)
