Supervised Learning

Supervised learning trains a model on input-output pairs — each training example comes with the "correct answer" — so the model learns a mapping it can apply to new, unlabeled inputs.

Regression vs Classification

Supervised learning splits further based on what kind of output you're predicting:

Minimal End-to-End Example

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import accuracy_score

data = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(
    data.data, data.target, test_size=0.2, random_state=42
)

model = LogisticRegression(max_iter=5000)
model.fit(X_train, y_train)             # learns from labeled training data

preds = model.predict(X_test)           # predicts on unseen data
print(accuracy_score(y_test, preds))    # compares predictions to true labels

Expected output: roughly 0.95–0.97 accuracy on this well-behaved, well-separated dataset — real-world data is rarely this clean, so treat unusually high accuracy as a signal to double-check for data leakage, not a reason to celebrate immediately.

Practical Use Cases

• Credit risk scoring, churn prediction, price forecasting
• Medical diagnosis support (given labeled historical cases)
• Email spam detection

Advantages

• Performance is directly measurable against ground truth
• Well understood, mature tooling, easy to evaluate and compare models

Limitations

• Requires labeled data, which is often expensive or slow to collect
• A model only learns patterns present in its training labels — biased or incomplete labels produce a biased model

Common Mistakes

• Evaluating on the same data used for training, which hides overfitting — always hold out a test set (train-test split).
• Treating a classification target as regression (or vice versa) without checking whether the output is really continuous or categorical.

Interview Relevance

Q: "How do you decide between regression and classification for a problem?" Look at the target variable: if it's a continuous number (price, temperature), it's regression; if it's a category (yes/no, class A/B/C), it's classification — even if the categories are numerically coded (0/1).

Practice Question

You're predicting whether a customer will renew a subscription (yes/no) and separately, how many days until they might cancel. Which problem is regression and which is classification?

Related ML Notes

• Unsupervised Learning
• Linear Regression
• Logistic Regression