Random search is a practical concept within Optimisation Algorithms. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
Random search is a practical concept within Optimisation Algorithms. It helps turn the broader workflow stage “8 · Hyperparameter Optimisation & Model Selection” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
The practical value of Random search comes from understanding both the transformation and the boundary around it: what information is allowed to enter, what assumption is being made, and how you know the result is still valid after the transformation.
A beginner-friendly way to reason about it is to start with a tiny case where the correct result can be checked independently. Once the mechanism is clear, scale the exact same reasoning to larger tables, pipelines or models.
Define what random search is meant to accomplish, identify the data or parameters it uses, apply it only where those inputs are valid, then inspect diagnostics and validate the effect on held-out or independent evidence.
The interactive view uses a concept-specific plot when the topic maps naturally to one; otherwise it uses a workflow view instead of leaving a broken placeholder.
In a small tabular project, document the choice of random search, apply it through a reproducible function or pipeline, and compare the downstream result with a simple baseline.
A useful diagnostic question is: Could the same code still run successfully if the analytical assumption were wrong? If yes, add an explicit validation check rather than relying on execution success.
Keep the example small enough that you can inspect each stage manually.
# Step 1 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from scipy.stats import loguniform
# Step 2 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.datasets import make_classification
# Step 3 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.linear_model import LogisticRegression
# Step 4 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.model_selection import RandomizedSearchCV
# Step 5 — Compute the right-hand expression and store its result in `X, y` for the next step.
X, y = make_classification(n_samples=120, n_features=6, random_state=4)
# Step 6 — Instantiate `search` with the chosen algorithm/configuration before fitting it to data.
search = RandomizedSearchCV(LogisticRegression(max_iter=1000), {"C": loguniform(1e-2, 1e2)}, n_iter=6, cv=3, random_state=4)
# Step 7 — Fit the model or transformer, learning its parameters from the supplied training data.
search.fit(X, y)
# Step 8 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 1 · candidates tried:", len(search.cv_results_["params"]))
# Step 9 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 2 · best C:", round(search.best_params_["C"], 3))
# Step 10 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · best CV score:", round(search.best_score_, 3))STEP 1 · candidates tried: 6 STEP 2 · best C: 1.545 STEP 3 · best CV score: 0.908