Back to course overview

Random Forest in Python - ML From Scratch 10

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[0] 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)[0][0] 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), 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)

FREE VS Code / PyCharm Extensions I Use

🪁 Code faster with Kite, AI-powered autocomplete: Link *

✅ Write cleaner code with Sourcery, instant refactoring suggestions: Link *

* These are affiliate links. By clicking on it you will not have any additional costs, instead you will support me and my project. Thank you! 🙏

Check out my Courses