Reference · How-to · ~10 min
How to train a scikit-learn classifier
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Train and evaluate a classic ML model in a few lines — great baseline before reaching for an LLM.
Train and evaluate a classic ML model in a few lines — great baseline before reaching for an LLM.
#Steps
1. **Load tabular data** — CSV with features + label column
2. **Train/test split** — `train_test_split(X, y, test_size=0.2, stratify=y)`
3. **Preprocess** — encode categoricals, scale numerics (fit on train only)
4. **Pick a model** — start with `LogisticRegression` or `RandomForestClassifier`
5. **Fit** — `model.fit(X_train, y_train)`
6. **Evaluate** — `classification_report`, confusion matrix on held-out test
7. **Save** — `joblib.dump(model, "model.joblib")` for reuse
#Sketch
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)
print(classification_report(y_test, clf.predict(X_test)))#Watch out
Leakage from fitting preprocessors on the full dataset — always pipeline through train split first.