Grid Search

• Run GridSearchCV over a sensible grid
• Explain combinatorial explosion and laggard-cost pitfalls
• Design grids that stay evaluable

• It's exhaustive within a defined space — you know your evaluation touched every corner.
• It is trivial to parallelize: each config is an independent job.
• Results are auditable ('we tried these 120 combos, this one won').
• It gives a complete response surface if you bother to inspect intermediate results.

Working through the whole combinatorial menu guarantees you find the best dish among what's offered — but the menu doubles every time you add a 'choice of side', and most of the plate is barely edible. Grid search is exhaustive, but it's only as smart as the grid you put in front of it.

• Pick the hyperparameters and candidate values: n_values per knob.
• Grid = ∏ n_values combinations (multiply every knob's list length).
• For each combo, run k-fold CV and record mean + spread of the score.
• Select the config with the best mean score (ties → smallest model / fastest train).
• Refit that config on all the data (or keep the best fold model) for deployment.

• A pricing model grid-searches 5 learning rates × 4 tree depths × 2 col_sample ratios on a small dataset — 40 CV fits finish in minutes.
• CI runs a minimal grid each night so default-config regressions surface before the weekly full-scale search.
• A feature-engineering team locks the data once, grids a model family to establish the baseline score everything else must beat.

• Use logarithmic or geometric spacing for magnitude-like knobs (learning rate 1e-4..1e-1, not 0.01, 0.02, 0.03).
• Start coarse (2–3 values per knob) to find the region, then zoom in.
• Always cross-validate each grid cell — a single split is noisy enough to misrank close cells.
• Record the full result table, not just the winner; examining the surface explains why.

• Making the grid enormous (10×10×10×10) and 'letting the cloud sort it out' — each failed run costs the same as successful ones.
• Gridding fine steps over a log-scale knob, wasting cells on under-visited magnitude regimes.
• Judging by best_score_ alone — adjacent cells often tie within the fold spread.
• Retyping cumbersome scoring/refit logic instead of using GridSearchCV's cv_results_ properly.

• Inspect cv_results_: if rank-1 and rank-10 cells are within fold noise, shrink the grid or reduce fold variance.
• Warm-start or parameterize model training so failed cells fail fast instead of wasting your whole budget.
• Plot mean_test_score heat-maps for two-knob grids to see ridges and plateaus.
• Confirm refit=True gives the deployed model the exact best config and no late copy-paste drift.