Using Random Search to Tune the Hyperparameters of a Random Decision Forest with Python

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Random search is an efficient method for automated hyperparameter tuning machine learning models. Hyperparameters are model properties (e.g., the number of estimators for an ensemble model). Unlike model parameters, the machine learning algorithm does not discover the model hyperparameters during training. Instead, we need to specify them in advance. Finding … Read more

Hyperparameter Tuning a Random Forest Classifier using Grid Search in Python

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Hyperparameters control how a machine learning algorithm learns and how it behaves. Unlike the internal parameters (coefficients, etc.) that the algorithm automatically optimizes during model training, hyperparameters are model characteristics (e.g., the number of estimators for an ensemble model) that we must set in advance. Finding the optimal hyperparameter configuration … Read more