> For the complete documentation index, see [llms.txt](https://gautamnaik1994.gitbook.io/snippets/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gautamnaik1994.gitbook.io/snippets/machine-learning/feature-engineering/feature-scaling.md).

# 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.