IsolationForest.yaml

# https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html
IsolationForest:
  n_estimators: 100 # The number of base estimators in the ensemble.
  max_samples: auto # If max_samples is larger than the number of samples provided, all samples will be used for all trees (no sampling).
  contamination: 0 # The amount of contamination of the data set, i.e. the proportion of outliers in the data set. Used when fitting to define the threshold on the scores of the samples.
  max_features: 1.0 # The number of features to draw from X to train each base estimator. 
  bootstrap: False # If True, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed.
  n_jobs: 1 # The number of jobs to run in parallel for both fit and predict.
  random_state: 42 # Controls the pseudo-randomness of the selection of the feature and split values for each branching step and each tree in the forest.
  verbose: True # Controls the verbosity of the tree building process.
  warm_start: False # When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.