- Block 18 — Professional pipelines
- Objective
- route a heterogeneous table column by column with a single
fitted object; measure what the hand-rolled alternative costs when the index
silently slips; master every form of a column specification, including the two
dtype traps pandas 3.0 introduced; decide
remainder deliberately; read
get_feature_names_out well enough to attribute a coefficient to the column it
came from; nest pipelines inside branches and address them through the
preprocessor__num__imputer__strategy path; choose sparse or dense output on
measured memory and fit time; use n_jobs where it pays; transform one column
several ways; encode datetime columns as cyclical features; place a text
vectorizer beside tabular branches without hitting the one-dimensional input
rule; and survive the four forms of column drift. - Estimated duration
- 135 minutes
- Prerequisites
- chapters 018 and 019 (missing values, imputation), 022
(categorical encoding), 023 (feature scaling), 024 (feature engineering), 026
(train, validation, test), 028 (data leakage), 029 (
fit, predict,
predict_proba), 034 (cross-validation), 035 (hyperparameter tuning), 036
(logistic regression), 063 (ROC and AUC), 076 (the classification pipeline) - Associated quizzes
079.1-quiz-heterogeneous-tables.md to
079.9-quiz-column-drift.md