Scikit-learn Random Forest

Create Random Forest model for classification or regression task. Please check Advanced options to set hyper parameters values. After model creation model should be fitted on data.

random-forestclassificationregression

Required packages

You need below packages to use the code generated by recipe. All packages are automatically installed in MLJAR Studio.

scikit-learn>=1.5.0

Interactive recipe

You can use below interactive recipe to generate code. This recipe is available in MLJAR Studio.

Python code

# Python code will be here

Code explanation

Create model for Random Forest algorithm with selected hyper parameters.

Please be aware that training time greatly depends on number of trees in the model and data size.

Example Python notebooks

Please find inspiration in example notebooks

Train Random Forest regressor
The `scikit-learn` provides implementation of [Random Forest](/glossary/random-forest/) ...
Train Random Forest classifier
Python implementation of Random Forest algorithm available in `scikit-learn` package ...

Scikit-learn cookbook

Code recipes from Scikit-learn cookbook.

Decision Tree
Random Forest
k-Nearest Neighbors
Train Model
Compute Predictions
Visualize Decision Tree
Hyper Parameters Search
Feature Importance
Compute Metric
Confusion Matrix
ROC Curve
Precision-Recall
Lift Chart
Calibration PLot

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