> For the complete documentation index, see [llms.txt](https://mugdha-thanawala.gitbook.io/tkmt_package/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://mugdha-thanawala.gitbook.io/tkmt_package/guides/ensemble-learning-techniques/averaging.md).

# Averaging

**Multiple predictions are made for each data point and the average of predictions from all the models are used to make the final prediction.**

{% hint style="info" %}
`from tkmt_package.ensemble import Averaging`
{% endhint %}

Averaging

{% hint style="info" %}
`Averaging.get_Averaging_technique(base_model, train_X, train_y, test_X)`
{% endhint %}

```python
Parameters
----------
base_model: models set as the base models for averaging.
train_X: input train data.
train_y: output train data.
test_X: input test data.

Example
-------
>>> from tkmt_package.ensemble import Averaging
>>> awe = Averaging()
>>> y_test_pred = awe.get_Averaging_technique(base_model= base_model,
                             train_X=x_train,
                             train_y= y_train,
                             test_X=x_test)
```