achieves_planned_objective data transformation class discovery data transformation class discovery objective random forest procedure # S3 method for formula randomForest(formula, data=NULL, ..., subset, na.action=na.fail) # S3 method for default randomForest(x, y=NULL, xtest=NULL, ytest=NULL, ntree=500, mtry=if (!is.null(y) && !is.factor(y)) max(floor(ncol(x)/3), 1) else floor(sqrt(ncol(x))), replace=TRUE, classwt=NULL, cutoff, strata, sampsize = if (replace) nrow(x) else ceiling(.632*nrow(x)), nodesize = if (!is.null(y) && !is.factor(y)) 5 else 1, maxnodes = NULL, importance=FALSE, localImp=FALSE, nPerm=1, proximity, oob.prox=proximity, norm.votes=TRUE, do.trace=FALSE, keep.forest=!is.null(y) && is.null(xtest), corr.bias=FALSE, keep.inbag=FALSE, ...) # S3 method for randomForest print(x, ...) from https://www.rdocumentation.org/packages/randomForest/versions/4.6-14/topics/randomForest sklearn.ensemble.RandomForestRegressor(n_estimators=10, criterion=’mse’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False) from: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion=’gini’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight=None) from: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html random forest adapted from: https://en.wikipedia.org/wiki/Random_forest and http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html Philippe Rocca-Serra random forest procedure is a type of data transformation used in classification and statistical learning using regression. The random forest procedure is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset (it operates by constructing a multitude of decision trees at training time) and use averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default). The random forest procedure outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. ready for release