Skip to content
v0.1.3latest release121 skills4 categories6 sub-categories247 tested scripts397 reference docsContact us
Vector Agentic Skills
Submit a skill

Pipeline Anatomy

Reference for scikit-learn-pipeline-builder · 96 lines

AI & Machine Learning / Classical Machine Learningv1.0.0Updated OCT 25 20265 references2 scripts

Pipeline and make_pipeline

python
from sklearn.pipeline import Pipeline, make_pipeline
pipe = Pipeline([("scale", StandardScaler()), ("clf", LogisticRegression(max_iter=2000))])
pipe = make_pipeline(StandardScaler(), LogisticRegression(max_iter=2000))   # names: standardscaler, logisticregression
  • All steps but the last must be transformers (fit + transform); the last can be anything.
  • pipe[:-1] is the preprocessing sub-pipeline; pipe[-1] / pipe.named_steps["clf"] the model.
  • "passthrough" or None disables a step (useful in searches: {"select": ["passthrough", SelectKBest(k=20)]}).

ColumnTransformer

python
from sklearn.compose import ColumnTransformer, make_column_selector as sel
ct = ColumnTransformer(
    transformers=[
        ("num", num_pipe, sel(dtype_include="number")),
        ("cat", cat_pipe, sel(dtype_include=["object", "category", "string"])),
        ("txt", TfidfVectorizer(), "description"),           # a single string column -> 1-D input for text
    ],
    remainder="drop",                # or "passthrough", or a transformer
    sparse_threshold=0.3,            # output sparse if overall density < 0.3
    verbose_feature_names_out=True,  # prefixes names with the transformer name: "num__age"
    n_jobs=None,
)
  • Column spec: list of names, a single name (1-D for text vectorisers), boolean mask, slice, or a selector.
  • Text vectorisers need a string, not a list: "description" not ["description"].
  • ct.get_feature_names_out() after fitting; ct.named_transformers_["cat"] to inspect a fitted block.

set_output

python
ct.set_output(transform="pandas")     # DataFrame out, with column names (sklearn >= 1.2)

Requires every inner transformer to support set_output (text vectorisers and some third-party transformers do not) and dense output. Useful for debugging and for models that read column names.

Caching

python
from tempfile import mkdtemp
pipe = Pipeline(steps, memory=mkdtemp())     # caches fitted transformers across a grid search

Helps when preprocessing is expensive and only the model's parameters vary.

FeatureUnion

Concatenates outputs of transformers applied to the same input:

python
from sklearn.pipeline import FeatureUnion
from sklearn.decomposition import PCA, TruncatedSVD
feats = FeatureUnion([("pca", PCA(n_components=10)), ("raw", "passthrough")])

FunctionTransformer

python
import numpy as np
from sklearn.preprocessing import FunctionTransformer
log1p = FunctionTransformer(np.log1p, inverse_func=np.expm1, feature_names_out="one-to-one")

Stateless only (learns nothing). Pass a module-level function, not a lambda, so the pipeline pickles.

TransformedTargetRegressor

python
from sklearn.compose import TransformedTargetRegressor
reg = TransformedTargetRegressor(regressor=pipe, func=np.log1p, inverse_func=np.expm1)

Transforms y for fitting and inverts predictions automatically.

Imputation inside pipelines

ImputerUse
SimpleImputer(strategy="median", add_indicator=True)default for numerics
SimpleImputer(strategy="most_frequent") or fill_value="missing" with strategy="constant"categoricals
KNNImputer(n_neighbors=5)small data, related features; needs scaling first
IterativeImputer (experimental import)MICE-style; slow; rarely worth it for GBMs
noneHistGradientBoosting, LightGBM, XGBoost handle NaN natively

Inspecting a fitted pipeline

python
pipe.fit(X, y)
names = pipe[:-1].get_feature_names_out()
coefs = pd.Series(pipe[-1].coef_.ravel(), index=names).sort_values()
pipe.set_params(clf__C=0.1)                 # same naming as search spaces
print(pipe.get_params().keys())             # every addressable parameter
from sklearn import set_config; set_config(display="diagram")   # HTML diagram in notebooks
More in Classical Machine Learning

anomaly-detection-model-builder

Build and evaluate anomaly detection for tabular, sensor and transaction data with few or no labels: define the anomaly type (point, contextual, collective), choose detectors (robust z-score/MAD and IQR rules, Mahalanobis with robust covariance, k-NN distance, LOF, Isolation Forest, one-class SVM, PCA reconstruction / autoencoders, forecasting residuals for time series), combine them by rank, explain each alert by its deviating features, set thresholds from an alert budget or the score distribution, and evaluate with precision@k, PR-AUC and recall on known incidents, or detector agreement when no labels exist. Includes pure-Python detectors (robust z, Mahalanobis, kNN, Isolation Forest, ensemble) and an evaluation script.

catboost-model-builder

Build CatBoost models that use its strengths correctly: raw categorical columns as cat_features (ordered target statistics, no manual encoding), text_features, Pool construction with NaN-safe categories, ordered boosting and has_time for temporal data, symmetric (oblivious) trees, overfitting detector with eval_set and use_best_model, losses for classification, regression (Quantile, Poisson, Tweedie) and ranking (YetiRank), class weights, monotone constraints, feature importance (PredictionValuesChange, LossFunctionChange) and SHAP values, GPU training and model export. Plans cat/text/numeric columns from a CSV and generates a runnable training script, and checks parameter sets for the conflicts CatBoost rejects (iterations vs n_estimators, Bayesian bootstrap with subsample, multiple class-weight options, depth > 16, use_best_model without eval_set).

categorical-encoding-selector

Choose and implement the right encoding for every categorical column given the model family and the data: one-hot (with infrequent-level grouping and handle_unknown), ordinal with an explicit order, cross-fitted target encoding (scikit-learn TargetEncoder, CatBoost ordered statistics, category_encoders inside CV), frequency/count encoding, feature hashing, native categorical support in LightGBM, HistGradientBoosting, XGBoost and CatBoost, prefix levels for codes (zip3, NAICS-2), and embeddings. Measures cardinality, rare-level share, unseen-level rate on a time-ordered holdout and target signal per column, emits the ColumnTransformer, and quantifies target-encoding leakage (in-sample vs out-of-fold vs holdout AUC).

classification-model-selector

Choose the right classification algorithm for tabular, text or mixed data and prove the choice with a fair bake-off: logistic regression (L2, L1, elastic net), linear SVM, naive Bayes, k-NN, RBF SVM, decision trees, random forest, extra trees, HistGradientBoosting, LightGBM, XGBoost, CatBoost, explainable boosting machines (EBM/GAM), points scorecards and MLPs, matched to data size, width, categorical and high-cardinality share, missingness, class imbalance, text, and constraints such as interpretability, monotonic constraints, calibrated probabilities, latency budgets and tiny deployable models. Ranks candidates with starting hyperparameters and cautions, then generates a bake-off script running every candidate on identical CV folds with per-fold metrics, fit time and prediction latency.

clustering-algorithm-selector

Choose the right clustering algorithm and check that clustering makes sense at all: cluster tendency (Hopkins statistic with a correlated-features caveat, nearest-neighbour isolation), then k-means / MiniBatchKMeans, Gaussian mixtures with BIC, agglomerative (Ward, average, complete, single) with dendrograms, DBSCAN, HDBSCAN, OPTICS, spectral clustering, BIRCH, mean shift, k-prototypes and k-modes for mixed or categorical data, and Gower distance, matched to data size, expected shapes, noise, varying density, known k, soft memberships, hierarchy and streaming. Covers preprocessing (scaling, encoding, dimensionality, distance metrics), validation (internal indices, bootstrap stability, external labels, business profiling) and assigning new points.

collaborative-filtering-implementer

Implement collaborative filtering recommenders that beat popularity: item-based kNN with cosine, Jaccard or asymmetric similarity, shrinkage, neighbourhood size, positive-only or mean-centred rating signals and 'because you liked X' explanations; implicit ALS with confidence weighting, BPR pairwise ranking and the closed-form EASE model in plain NumPy, tuned on a temporal validation split by recall@k and NDCG@k and refit on all data; plus user-kNN, explicit matrix factorisation, the implicit/LightFM libraries, hyperparameter ranges, cold-start fallbacks, scaling with sparse matrices and approximate nearest neighbours. Produces top-k lists in the format of the recommendation evaluator.