Supervised Machine Learning

In supervised learning, the model learns from labeled training data, making predictions based on known input-output pairs. This section explores several key algorithms with interactive demos running directly in your browser.

Naive Bayes

A probabilistic classifier based on Bayes' Theorem with independence assumptions between predictors. It is highly scalable and particularly good for high-dimensional datasets.

K-Nearest Neighbors

KNN is a simple, versatile algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions).

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)

Artificial Neural Networks

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns.