Supervised Learning

Supervised learning is the most common and widely used type of machine learning. In supervised learning, the algorithm is trained on labelled data — a dataset where each example has both input features and a known correct output (the label). The goal is to learn a mapping function that can predict the output for new, unseen inputs.

---

How Supervised Learning Works

The supervised learning process follows these steps:

1. Collect labelled data — Each example has features (X) and a known label (y)
2. Split the data — Divide into training set and test set
3. Choose an algorithm — Select a model appropriate for the task
4. Train the model — The algorithm learns patterns from the training data
5. Evaluate — Test the model on unseen data to measure its performance
6. Predict — Use the trained model to make predictions on new data

from sklearn.model_selection import train_test_split

# X = features, y = labels
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

model.fit(X_train, y_train)       # Train
predictions = model.predict(X_test)  # Predict

---

Classification