The model
Empirically, generalisation error falls as a power law in the training size, so a score that rises with data follows
val(n) = a - b * n^(-c), b >= 0, c > 0ais the asymptote: the score this model class reaches with unlimited data from the same distribution.cis the learning rate of the curve; 0.3-0.7 is typical for tabular models.bscales how far the current curve is from the asymptote.
diagnose_curve.py grid-searches c (0.005-1.5) and solves a and b by least squares on the largest four
sizes. The smallest sizes are often in a different regime and pull the fit around. The asymptote is capped at
the final training score, because training descends towards the same limit. This fit matches
scipy.optimize.curve_fit with the same bounds.
How far to trust it
| Extrapolation | Reliability |
|---|---|
| within the measured range | interpolation, good |
| 2x the largest size | usually within the fold noise |
| 4x | a guess; the exponent is poorly determined from four points |
| 10x and beyond | a direction only; curves often flatten earlier than the power law says |
Requirements:
- new data from the same distribution, with the same labelling process and quality
- R^2 of the fit >= 0.9 (the script warns otherwise and falls back to the measured slope capped by headroom)
- enough folds that the fold standard error is smaller than the gain being predicted
Is collecting data worth it?
value of doubling = (predicted gain from 2x) x (value per metric point)
cost of doubling = rows x labelling cost per row + training cost growth (fit_time column)Translate the metric into money with the metric-to-business mapping from ml-success-metric-selector: e.g. +0.02 recall at fixed precision x fraud loss per missed case.
Alternatives when data is the bottleneck
| Option | When |
|---|---|
| Active learning (label the uncertain rows) | labels are expensive, unlabelled rows are plentiful |
| Weak supervision / labelling rules | experts can write heuristics faster than labels |
| More positives only | imbalanced targets: the curve is driven by the minority class |
| Pretrained embeddings / transfer | text, images, product catalogues |
| Stronger priors: monotonic constraints, simpler model | small data and high variance |
| Data augmentation | images/audio; rarely meaningful for tabular data |
| Longer history | time series, if the old data is still relevant (check with a recency test) |
Sample size for a target
With --target, the script solves n = (b / (a - target))^(1/c). If the target is at or above the
asymptote it reports that more data alone cannot reach it. Present it as an order of magnitude, together
with the 2x prediction you can test cheaply.
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.