Full API

.recommend()

Analyze your dataset and print a tailored recommendation. Can be called before or after .train().

Usage

python
model.recommend()

Output

output
Model Recommendations
──────────────────────────────────────────────────────
1. Best for your data size: LogisticRegression
   → Fast, interpretable, works great on small datasets.
2. Most interpretable: DecisionTreeClassifier
3. Most accurate: RandomForestClassifier
4. Skip: GradientBoosting - overkill for small data.

Tip: Start simple, then try complex models only if needed.