Zero → Max AI

EP06 · 8 min

Classic ML tour: linear/logistic, trees, random forests, boosting

Build intuition for when to use core classical models before reaching for larger systems.

Simple definition
Classical ML models are efficient predictors with strong baselines and good interpretability.
Precise definition
Classical learners optimize structured objective functions over engineered features and often deliver strong tabular performance with low inference cost.

Objective

You should leave with a practical model selection heuristic for common tabular tasks.

Model quick tour

  • Linear regression: fast numeric prediction, good baseline.
  • Logistic regression: classification baseline with interpretable weights.
  • Decision trees: rule-like structure, handles nonlinearity.
  • Random forest: many trees reduce variance.
  • Gradient boosting: sequentially corrects residual mistakes, often top performance on tabular data.

Worked example (online store)

Delivery time prediction often starts with linear regression for clarity. Then a tree-based model captures interactions like "rain + rush hour + long distance".

Spam classification can start with logistic regression and text features. If performance stalls, move to boosted trees or neural text models depending on latency and quality targets.

Practical model strategy

  1. Start simple.
  2. Measure.
  3. Increase complexity only when metrics justify it.
  4. Keep a baseline in production comparisons.

This keeps you honest and helps detect regressions.

Three takeaways

  • Baselines are strategic, not temporary.
  • Choose models based on constraints, not hype.
  • Keep evaluation protocol identical across model comparisons.

Visual Stage

Interactive walkthrough

Visual walkthrough: classic model families

Tap each model type for a practical usage note.

Step Insight

Fast, interpretable baselines that work well when relationships are mostly additive.

Common traps
  • Skipping baseline models and jumping to complexity.
  • Ignoring interpretability requirements.
  • Using boosted models without monitoring drift.
Three takeaways
  • Model choice depends on data shape, latency, and explainability needs.
  • Trees capture nonlinearity with minimal feature scaling.
  • Strong baselines reduce waste and guide later complexity.
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