Hyperparameters - utkaln/machine-learning GitHub Wiki

• Hyperparameters in a Random Forest algorithm are parameters that are set prior to training the model, and they control the learning process of the model.
• They are used to optimize the performance of the model by controlling the complexity of the trees and the overall performance of the Random Forest.
• Some common hyperparameters in Random Forest include:
  • n_estimators: The number of trees in the forest.
  • max_depth: The maximum depth of the tree, i.e., the maximum number of levels in each decision tree.
  • min_samples_split: The minimum number of samples required to split an internal node.
  • min_samples_leaf: The minimum number of samples required to be at a leaf node.
  • max_features: The maximum number of features to consider when looking for the best split.
  • criterion: The criterion used to evaluate the quality of a split. The commonly used criterion are "gini" and "entropy."
  • n_jobs: The number of jobs to run in parallel for both fit and predict. Uses more number of CPUs

Example Usage