ML Types

Also Known As

TL;DR

Explanation

Supervised: labelled input→output pairs. Classification (spam/not), regression (predict price). Unsupervised: clustering (K-means), dimensionality reduction, anomaly detection. Self-supervised: model generates its own labels from data — GPT predicts next token. Reinforcement learning: agent+rewards+policy — game playing, RLHF for fine-tuning LLMs. Choosing the right paradigm depends on: whether labels are available, whether you need groups or predictions, and latency requirements.

Common Misconception

Why It Matters

Common Mistakes

• Supervised learning without sufficient labelled data — model learns noise
• Using clustering when supervised labels are available — worse results
• Ignoring class imbalance in supervised classification
• Not considering self-supervised approaches when labelling is expensive

Code Examples

// Goal: detect fraud (labelled historical data exists)
// Wrong choice: unsupervised clustering (ignores labels)
// Should use: supervised binary classification with fraud labels

// Matching ML type to problem:
// Churn prediction (labelled) → supervised: logistic regression
// Customer segments (no predefined groups) → unsupervised: K-means
// Optimise recommendations (engagement signal) → reinforcement: bandit
// PHP code completion (large PHP codebase) → self-supervised: next-token

References

• ↗ https://scikit-learn.org/stable/user_guide.html

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