Removed / renamed parameters (all raise in 1.9)
| Old | New | Removed in |
|---|---|---|
OneHotEncoder(sparse=False) | sparse_output=False | 1.4 |
LogisticRegression(penalty="none") | penalty=None | 1.4 |
normalize=True (LinearRegression, Ridge, Lasso...) | StandardScaler step | 1.2 |
base_estimator= (Bagging, AdaBoost, CalibratedClassifierCV...) | estimator= | 1.4 |
max_features="auto" (forests, trees) | "sqrt" (classifiers), 1.0 (regressors) | 1.3 |
criterion="mse" / "mae" | "squared_error" / "absolute_error" | 1.2 |
loss="deviance" (GradientBoosting), "ls", "lad" | "log_loss", "squared_error", "absolute_error" | 1.3 |
SGDClassifier(loss="log") | loss="log_loss" | 1.3 |
AgglomerativeClustering(affinity=) | metric= | 1.4 |
mean_squared_error(squared=False) | root_mean_squared_error() | 1.6 |
cross_val_score(fit_params=) | params= | 1.6 |
AdaBoostClassifier(algorithm="SAMME.R") | (only SAMME; parameter later removed) | 1.6+ |
LogisticRegression(multi_class=) | multinomial is the default; OneVsRestClassifier for OvR | deprecated 1.5 |
TargetEncoder(shuffle=, random_state=) | pass a CV splitter as cv= | deprecated 1.9 |
Run pipeline_lint.py (SK002) after upgrades.
Behaviour changes worth knowing
| Change | Impact |
|---|---|
HistGradientBoosting* categorical_features="from_dtype" default (1.6) | pandas category columns are treated as categorical automatically |
OneHotEncoder handle_unknown="infrequent_if_exist", min_frequency, max_categories (1.1) | rare-level grouping without custom code |
set_output(transform="pandas") (1.2), "polars" (1.4) | DataFrame outputs with names |
| Metadata routing (1.3+, opt-in) | sample weights and groups routed explicitly |
TunedThresholdClassifierCV, FixedThresholdClassifier (1.5) | threshold selection as an estimator |
root_mean_squared_error, d2_* scores (1.4+) | new metric functions |
validate_data function replaces self._validate_data (1.6) | custom estimators |
Error messages and fixes
| Message | Cause | Fix |
|---|---|---|
Found unknown categories [...] in column ... during transform | encoder without handle_unknown | handle_unknown="ignore" / "infrequent_if_exist" / "use_encoded_value" |
The feature names should match those that were passed during fit | columns renamed/reordered | X.reindex(columns=train_columns) |
X has N features, but ... is expecting M features | different preprocessing at inference | use the saved Pipeline, not separate steps |
ConvergenceWarning: lbfgs failed to converge | unscaled features, low max_iter | scale; max_iter=2000+; check regularisation |
Input X contains NaN | estimator without native NaN support | SimpleImputer step, or HistGradientBoosting |
could not convert string to float | categorical column not encoded | add it to the ColumnTransformer |
Can't pickle <function <lambda>> | lambda in FunctionTransformer / custom params | module-level function |
n_components must be <= n_features | SVD/PCA on a small vocabulary or fold | cap components (see TextSVD in gen_pipeline output) |
All the N fits failed | an error inside CV folds | error_score="raise" to see the first traceback |
Gotchas
predictreturns labels; ranking metrics needpredict_proba(X)[:, 1]ordecision_function.classes_order definespredict_probacolumns; for string labels checkmodel.classes_.train_test_splitshuffles by default; time-ordered data needsshuffle=Falseor explicit cutoffs.StratifiedKFoldwithoutshuffle=Truekeeps data order within classes (often sorted data -> biased folds).cross_val_scoreusesStratifiedKFoldfor classifiers whencv=int, unshuffled.LogisticRegressionregularises by default (C=1.0); unscaled features are penalised by units.RandomForest*impurity importances are biased; use permutation importance on held-out data.
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.