Rääkige mulle RandomForestist: kuidas see on üles ehitatud, kuidas see erineb tavalisest puude baggingust.

RandomForest is an ensemble machine learning method that builds multiple decision trees and combines their predictions to improve the model's quality and stability.

Main features of RandomForest:

• Each tree is trained on a random sample of the original data with replacement (bagging).
• During the construction of each tree, at each node, a random subset of features is selected, and the best one is chosen for splitting.

Differences from traditional tree bagging:

• In traditional bagging, trees are built on random data subsets, but the feature used for splitting is the entire feature set.
• In RandomForest, additional randomness is introduced in feature selection at each node, reducing correlation between trees and increasing generalization ability.

Python example using sklearn:

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100, max_features='sqrt')
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Here, the parameter max_features='sqrt' sets a random subset of features for each split.