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A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.
- sklearn.ensemble.RandomForestRegressor
A random forest is a meta estimator that fits a number of...
- sklearn.ensemble.RandomForestRegressor
A random forest is a meta estimator that fits a number of decision tree regressors on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.
Learn how to use random forests for classification in Python with scikit-learn. This tutorial covers the workflow, hyperparameter tuning, and evaluation of random forests with examples and visualizations.
Aprende cómo crear y usar modelos Random Forest con Python y scikit-learn. Descubre sus ventajas, desventajas, parámetros y ejemplos de regresión y clasificación.
Aprende a usar los bosques aleatorios para la clasificación en Python con scikit-learn. Este tutorial explica cómo funcionan los bosques aleatorios, cómo ajustarlos y entrenarlos, y cómo evaluar su rendimiento.
5 de ene. de 2022 · Learn how to use random forests, an ensemble algorithm that reduces overfitting by creating multiple decision trees, to classify data. This tutorial covers how to deal with missing and categorical data, how to create and visualize random forests, and how to evaluate their performance.
24 de feb. de 2021 · Learn how to build a coffee rating classifier with sklearn using random forest, a supervised learning method that consists of multiple decision trees. See how to perform data exploration, data augmentation, and model evaluation with sklearn.
