Are you looking for how to choose n_estimators in the random forest? Actually, * n_estimators * defines in the underline decision tree in Random Forest. See ! the Random Forest algorithms is a bagging Technique. Where we ensemble many weak learn to decrease the variance. The

*is a hyperparameter for Random Forest. So In order to tune this parameter, we will use*

**n_estimators***. In this article, We will explore the implementation of*

**GridSearchCV***for*

**GridSearchCV***in random forests.*

**n_estimators**## Choosing n_estimators in the random forest ( Steps ) –

Let’s understand the complete process in the steps. We will use sklearn Library for all baseline implementation.

### Step 1-

Firstly, The prerequisite to see the implementation of hyperparameter tuning is to import the GridSearchCV python module.

`from sklearn.model_selection import GridSearchCV`

### Step 2-

Secondly, Here we need to define the range for n_estimators. With GridSearchCV, We define it in a param_grid. This param_grid is an ordinary dictionary that we pass in the GridSearchCV constructor. In this dictionary, We can define various hyperparameter along with n_estimators.

```
param_grid = {
'n_estimators': [100, 200, 300, 1000]
}
```

### Step 3 –

To sum up, this is the final step where define the model and apply GridSearchCV to it.

```
random_forest_model = RandomForestRegressor()
# Instantiate the grid search model
grid_search = GridSearchCV(estimator = random_forest_model , param_grid = param_grid, cv = 3, n_jobs = -1)
```

We invoke * GridSearchCV() *with the param_grid. The n_jobs = -1 indicates utilizing all the cores of the system.

Now once we call the ‘grid_search.best_params_ ‘, It will give you the optimal number for n_estimators for the Random Forest. We may use the RandomSearchCV method for choosing n_estimators in the random forest as an alternative to GridSearchCV. This will also give the best parameter for Random Forest Model.

## Random Forest Hyperparameters :

Most importantly, Here is the complete syntax for Random Forest Model. You may see the default values for n_estimators.

`class sklearn.ensemble.RandomForestClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)`

## Conclusion –

Most Importantly, this implementation must have cleared you how to choose n_estimators in the random forest. If you still facing any difficulties with n_estimators and their optimal value, Please comment below. Above all, If you want to keep reading an article on These bagging and boosting Algorithms, Please subscribe to us.

**Thanks**

**Data Science Learner Team**

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