Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning

Source: Dev.to

Model Evaluation

Start with basic model evaluation — quick tests that tell if a model is honest or just lucky.

When you have little data, use methods made for tiny sets, because some shortcuts break down fast on small datasets.

Cross‑Validation

Cross‑validation splits data differently to see how stable results are. Choosing the number of splits is a balance; it can change the outcome sometimes.

Bootstrap

If you want to know how much results vary, the bootstrap is a handy trick to estimate that variability.

Algorithm Selection

When you compare many methods, careful rules for algorithm selection keep you from picking a winner by accident.

These practical tips can help you avoid overfitting, false hope, and wasted work. Try a few and watch which choices actually improve results—testing smarter beats guessing every time.

Reference

Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning

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