AI & Machine Learning · Quick Reference

lesson-1.3 Cheat Sheet

AI & Machine Learning

In one line: A Pipeline chains a sequence of steps — typically several transformers followed by one final estimator — into a single object exposing the same fit / predict interface as any ot...

Key Ideas

1ColumnTransformer for mixed-type data. Most real datasets mix numeric columns (needing scaling) with categorical columns (needing encoding). ColumnTransformer applies different transformers to different col...

2Why pipelines matter beyond convenience. They make cross-validation safe by construction. If you preprocess your whole dataset once and then run cross-validation, each fold's "test" portion was preprocessed u...

Code Examples

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('clf', LogisticRegression())
])
pipe.fit(X_train, y_...

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder

preprocessor = ColumnTransformer([
    ('num', StandardScaler(), numeric_cols),
    ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_cols)
])

...

from sklearn.model_selection import cross_val_score

scores = cross_val_score(full_pipe, X, y, cv=5)
# Each fold refits preprocessor + model on that fold's train split only