Supervised Learning: Concepts, Patterns, and Pitfalls

ML foundations • 6-9 min read • 1-3 hours to prototype

TL;DR: Supervised learning trains models to map inputs to labeled outputs. Choose algorithms based on data size, feature types, and the need for interpretability.

When to use supervised learning

• You have labeled data (every training example has an associated target).
• You can define a clear metric to optimize (accuracy, recall, MAPE).

Common algorithms and trade-offs

• Linear models (logistic regression, linear regression): interpretable, fast, good baseline.
• Decision trees: intuitive, handle mixed data types, prone to overfitting if deep.
• Ensembles (random forest, gradient boosting): strong performance, less interpretable.
• Neural networks: flexible for large, high-dimensional data (images, text).

Evaluation strategies

• Cross-validation for small datasets.
• Time-based splits for temporal problems.
• Stratified sampling for imbalanced classes.

Visual: model comparison diagram

Replace with a small diagram comparing linear model / tree / ensemble / neural net trade-offs.

Pitfalls to avoid

• Data leakage: using features that contain future info.
• Target leakage via preprocessing fitted on full data.
• Ignoring class imbalance: choose proper metrics (precision/recall, F1).

Practical recommendation

1. Always start with a simple, interpretable model.
2. Create a solid baseline and only increase complexity when it improves the metric.
3. Track and log experiments to ensure reproducibility.

What’s next

We’ll build a simple decision tree model step-by-step in the next article and run it on a toy dataset.