# Categorical Encoding

## Target Encoding

* **High-cardinality features**: A feature with a large number of categories can be troublesome to encode: a one-hot encoding would generate too many features and alternatives, like a label encoding, might not be appropriate for that feature. A target encoding derives numbers for the categories using the feature's most important property: its relationship with the target.
* **Domain-motivated features**: From prior experience, you might suspect that a categorical feature should be important even if it scored poorly with a feature metric. A target encoding can help reveal a feature's true informativeness.

```python
from sklearn.preprocessing import TargetEncoder
```

## Label Encoding

Can be used for features with 2 categories.

## One Hot Encoder

Good when there are more than 2 categories but will result in a sparse matrix when there are a large number of categories

## Feature Binning

When there are a lot of features like above 20, we can try binning them into 2 or 3 categories. for example: different car models can be binned into sports, sedans, and luxury depending on the problem statement.

## Time and Date Transformation

```python
df['hr_sin'] = np.sin(df.hr*(2.*np.pi/24))
df['hr_cos'] = np.cos(df.hr*(2.*np.pi/24))
df['mnth_sin'] = np.sin((df.mnth-1)*(2.*np.pi/12))
df['mnth_cos'] = np.cos((df.mnth-1)*(2.*np.pi/12))
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://gautamnaik1994.gitbook.io/snippets/machine-learning/feature-engineering/categorical-encoding.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.