> For the complete documentation index, see [llms.txt](https://lauradang.gitbook.io/notes/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lauradang.gitbook.io/notes/machine-learning/scikit-learn/random-forests.md).

# Random Forests

#### Difficulties with trees

* More leaves lead to overfitting
* Less leaves lead to inaccurate predictions (cannot capture as many patterns and distinctions)

#### What is a random forest?

* uses many trees
* makes prediction by averaging the predictions of each component tree
* better predictive accuracy than a single decision tree

```python
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error

forest_model = RandomForestRegressor(random_state=1)
forest_model.fit(train_X, train_y)
melb_preds = forest_model.predict(val_X)
print(mean_absolute_error(val_y, melb_preds))
```