Compare supervised, unsupervised and reinforcement learning, and identify a typical application of each

What this dot point is asking

NESA wants you to compare the three main paradigms of machine learning, identify what kind of training data each needs, and give a typical application of each.

The answer

Supervised learning

The training data has both features and labels. The algorithm learns the function from features to label. Two sub-categories:

• Classification: the label is a category. Spam vs not spam. Cat vs dog. The output is a class.
• Regression: the label is a number. Predicting house prices, exam marks, stock prices. The output is a continuous value.

Common algorithms: logistic regression, decision trees, random forests, gradient-boosted trees, support vector machines, neural networks.

Applications:

• Email spam filtering (classification).
• Image classification (classification).
• House price prediction (regression).
• Disease diagnosis from medical imaging (classification).
• Forecasting daily power demand (regression).

Unsupervised learning

The training data has features but no labels. The algorithm finds structure in the data on its own. Sub-categories:

• Clustering: group similar examples together. K-means is the textbook algorithm.
• Dimensionality reduction: compress many features into a few. Principal Component Analysis (PCA), t-SNE.
• Anomaly detection: flag examples that are very different from the rest.

Applications:

• Customer segmentation in marketing.
• Recommendation engines (people similar to you also liked...).
• Fraud detection (transactions that look unusual).
• Topic modelling on a corpus of documents.

Reinforcement learning

An agent learns by interacting with an environment, receiving rewards or penalties for its actions. The algorithm learns a policy that maximises long-term reward.

Vocabulary:

• Agent: the learner.
• Environment: the world the agent acts in.
• State: a snapshot of the environment.
• Action: a choice the agent makes.
• Reward: feedback after each action.
• Policy: the strategy the agent learns.

Applications:

• Game playing (AlphaGo, Chess engines, video games).
• Robotics (a robot learning to walk).
• Autonomous driving decisions.
• Resource scheduling and operations research.

A worked Python example: supervised classification

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

data = load_breast_cancer()
X = data.data  # 30 features per tumour
y = data.target  # 0 = malignant, 1 = benign

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

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(classification_report(y_test, predictions))

A worked example: unsupervised clustering

from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans

X, _ = make_blobs(n_samples=300, centers=4, random_state=42)

# No labels passed to fit.
model = KMeans(n_clusters=4, random_state=42, n_init=10)
clusters = model.fit_predict(X)

# `clusters` assigns each example to one of 4 groups, learned from structure alone.

The same data is being analysed, but kmeans.fit_predict(X) takes only features, while RandomForestClassifier.fit(X, y) takes features and labels.

How to choose

Most real systems combine paradigms. A recommendation engine might use unsupervised clustering to discover taste groups, supervised regression to predict ratings, and reinforcement learning to optimise long-term engagement.