scikit-learn

Overview

Scikit-learn is a Python machine learning library that provides a consistent API for the full ML workflow: data preprocessing (scaling, encoding, imputation), model selection (classification, regression, clustering), hyperparameter tuning (grid search, randomized search), cross-validation, and pipeline construction. It supports serialization via joblib for production deployment.

Instructions

• When preprocessing data, use

  ColumnTransformer

  to apply different transformers to numeric and categorical columns (StandardScaler, OneHotEncoder, SimpleImputer), always within a Pipeline to prevent data leakage.
• When choosing models, start with fast baselines (LogisticRegression, RandomForest) and use

  HistGradientBoostingClassifier

  for best tabular performance, since it handles missing values natively and is faster than GradientBoosting.
• When evaluating, use

  cross_val_score

  with 5-fold CV instead of single train/test splits, and use

  classification_report()

  instead of accuracy alone since accuracy is misleading on imbalanced datasets.
• When tuning hyperparameters, use

  RandomizedSearchCV

  when the search space exceeds 100 combinations (faster than exhaustive GridSearchCV), and use

  StratifiedKFold

  or

  TimeSeriesSplit

  as appropriate.
• When building pipelines, chain preprocessing and model steps with

  Pipeline

  to ensure transformers fit only on training data, then serialize the full pipeline with

  joblib.dump()

  for deployment.
• When selecting features, use

  permutation_importance()

  for model-agnostic measurement,

  SelectKBest

  for statistical filtering, or

  feature_importances_

  from tree-based models.

Examples

Example 1: Build a customer churn prediction pipeline

User request: "Create a model to predict which customers will churn"

Actions:

1. Build a

   ColumnTransformer

   with

   StandardScaler

   for numeric features and

   OneHotEncoder

   for categorical
2. Create a

   Pipeline

   with the transformer and

   HistGradientBoostingClassifier

3. Tune hyperparameters with

   RandomizedSearchCV

   using

   StratifiedKFold

4. Evaluate with

   classification_report()

   focusing on recall for the churn class

Output: A tuned churn prediction pipeline with preprocessing, model, and evaluation metrics.

Example 2: Cluster customers into segments

User request: "Segment customers based on purchasing behavior"

Actions:

1. Preprocess features with

   StandardScaler

   in a pipeline
2. Use

   KMeans

   with silhouette score analysis to determine optimal cluster count
3. Run

   PCA

   for dimensionality reduction and visualization
4. Profile clusters with

   groupby

   on original features to interpret segments

Output: Customer segments with labeled profiles and a visual cluster map.

Guidelines

• Always use

  Pipeline

  to prevent data leakage by fitting transformers only on training data.
• Use

  ColumnTransformer

  for mixed data types: numeric scaling and categorical encoding in one object.
• Use

  HistGradientBoostingClassifier

  over

  GradientBoostingClassifier

  since it is faster and handles missing values natively.
• Use

  cross_val_score

  with 5-fold CV rather than a single train/test split since single splits are noisy.
• Use

  RandomizedSearchCV

  when the search space exceeds 100 combinations.
• Use

  classification_report()

  not just accuracy, which is misleading on imbalanced datasets.
• Serialize the full pipeline with

  joblib

  , not just the model, since deployment needs preprocessing too.