Yahoo Search Búsqueda en la Web

Entrar

Resultado de búsqueda

  1. scikit-learn.org › stable › modulesRandomForestClassifier — scikit-learn 1.5.1 documentation

    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.

  2. scikit-learn.org › stable › modulesRandomForestRegressor — scikit-learn 1.5.1 documentation

    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.

  3. www.datacamp.com › tutorial › random-forests-classifier-pythonRandom Forest Classification with Scikit-Learn | DataCamp

    This article covers how and when to use Random Forest classification with scikit-learn. Focusing on concepts, workflow, and examples. We also cover how to use the confusion matrix and feature importances.

  4. cienciadedatos.net › documentos › py08_random_forest_pythonRandom Forest python - Ciencia de datos

    Tutorial con teoría y ejemplo práctico de modelos Random Forest con python y scikitlearn

  5. www.datacamp.com › es › tutorialClasificación de bosques aleatorios con Scikit-Learn | DataCamp

    Este artículo trata de cómo y cuándo utilizar la clasificación Random Forest con scikit-learn. Centrado en conceptos, flujo de trabajo y ejemplos. También veremos cómo utilizar la matriz de confusión y las importancias de las características.

  6. datagy.io › sklearn-random-forestsIntroduction to Random Forests in Scikit-Learn (sklearn) - datagy

    5 de ene. de 2022 · Random forests are an ensemble machine learning algorithm that uses multiple decision trees to vote on the most common classification; Random forests aim to address the issue of overfitting that a single tree may exhibit; Random forests require all data to be numeric and non-missing

  7. towardsdatascience.com › a-practical-guide-to-implementing-a-random-forestA Practical Guide to Implementing a Random Forest Classifier in...

    24 de feb. de 2021 · A random forest—as the name suggests—consists of multiple decision trees each of which outputs a prediction. When performing a classification task, each decision tree in the random forest votes for one of the classes to which the input belongs.