t-SNE
- Converts high-dimensional distances into neighbour probabilities with a Gaussian per point whose width is set so its entropy matches log2(perplexity).
- Places points in 2-D, with a heavy-tailed Student-t kernel, to match those probabilities (KL divergence).
- The heavy tail pushes dissimilar points far apart: clusters look well separated regardless of their real gap.
Parameters:
perplexity(5-50; < n/3): roughly the number of effective neighbours. Small values fragment data into many small groups; large values show more global structure and run slower.early_exaggeration(12): separates clusters early in optimisation; rarely changed.learning_rate="auto"(max(n/early_exaggeration/4, 50)),max_iter1000+ (n_iterwas renamed).init="pca": deterministic-ish, better global layout than random.- Barnes-Hut (
method="barnes_hut") is O(n log n); exact is O(n²). - scikit-learn's TSNE has no
transformfor new points; openTSNE does.
UMAP
- Builds a fuzzy k-nearest-neighbour graph (n_neighbors) in the original space with local distance scaling.
- Optimises a low-dimensional layout whose fuzzy graph matches it (cross-entropy), with negative sampling.
Parameters:
n_neighbors(5-50, default 15): local vs global balance.min_dist(0.0-0.99, default 0.1): minimum spacing in the layout; 0.0 for clustering, larger to spread points for display.metric: any of euclidean, cosine, manhattan, correlation, haversine, custom numba functions.n_components: 2-3 for plots, 5-20 as input to clustering.random_statemakes it reproducible but single-threaded; omit it (and setn_jobs) for speed.densmap=True: preserves local density (sizes become more meaningful).transform(X_new): embeds new points into an existing map; inverse_transform for some metrics.
PCA
Linear projection on maximum-variance directions; distances and global layout are preserved as far as two dimensions allow. Always show it as the baseline and report its explained variance.
Pre-reduction
For > 50 features (embeddings with 384-1536 dims, many tabular features), PCA to 50 components first: removes noise dimensions, speeds up k-NN, and rarely changes UMAP/t-SNE results materially. For cosine-based embeddings L2-normalise first.
Other methods
| Method | Strength |
|---|---|
| PaCMAP | balances local and global structure; robust defaults |
| TriMap | triplet constraints; better global structure |
| openTSNE | fast FFT-accelerated t-SNE, transform, exaggeration control |
| densMAP | UMAP with density preservation |
| Parametric UMAP | neural-network mapping; embeds new points quickly |
| MDS / Isomap | distance / geodesic preservation; small data |
Large data
- UMAP handles millions of points (approximate nearest neighbours via pynndescent).
- t-SNE: openTSNE or FIt-SNE for > 100k points; subsample for exploration.
- Plot with alpha / datashader to avoid overplotting.
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.