Random Forest in Python - ML From Scratch 10
Implement a Random Forest algorithm using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.
In this Machine Learning from Scratch Tutorial, we are going to implement a Random Forest algorithm using only built-in Python modules and numpy. We will also learn about the concept and the math behind this popular ML algorithm.
All algorithms from this course can be found on GitHub together with example tests.
import numpy as np from collections import Counter from decision_tree import DecisionTree def bootstrap_sample(X, y): n_samples = X.shape idxs = np.random.choice(n_samples, n_samples, replace=True) return X[idxs], y[idxs] def most_common_label(y): counter = Counter(y) most_common = counter.most_common(1) return most_common class RandomForest: def __init__(self, n_trees=10, min_samples_split=2, max_depth=100, n_feats=None): self.n_trees = n_trees self.min_samples_split = min_samples_split self.max_depth = max_depth self.n_feats = n_feats self.trees =  def fit(self, X, y): self.trees =  for _ in range(self.n_trees): tree = DecisionTree(min_samples_split=self.min_samples_split, max_depth=self.max_depth, n_feats=self.n_feats) X_samp, y_samp = bootstrap_sample(X, y) tree.fit(X_samp, y_samp) self.trees.append(tree) def predict(self, X): tree_preds = np.array([tree.predict(X) for tree in self.trees]) tree_preds = np.swapaxes(tree_preds, 0, 1) y_pred = [most_common_label(tree_pred) for tree_pred in tree_preds] return np.array(y_pred)
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