1. To implement a Random forest for classification, you can use the DecisionTreeClassifier from sklearn as base trees. Data to check using load_breast_cancer, constructor parameters
   n_estimators – number of trees
   max_features – percentage of features (columns), for training each tree
   max_samples – percentage of samples (rows), for training each tree
   max_depth – depth of the tree
2. Implement cross validation on N folds
   3.Calculate the quality of the model on cross validation for load_breast_cancer data
   There should be a RandomForest class (with fit and predict methods) and a CrossValidation class

I used a forest from sklern, but the teacher rejected my work