scikit-learn contents

Section	Title	Contents
00	Getting Started	Estimators, Transformers, Preprocessors, Pipelines, Model Evaluation, Parameter Searches, Next Steps
01	Linear Models	OLS, Ridge, Lasso, Elastic-Net, Least Angle Regression (LARS), LARS Lasso, OMP, Naive Bayes, Generalized Linear Models (GLM), Tweedie Models, Stochastic Gradient Descent (SGD), Perceptrons, Passive-Aggressive Algos, Polynomial Regression
01a	Logistic Regression	Basics, Examples
01b	Splines	Polynomial Regression & Basis Functions, Periodic splines
01c	Quantile Regression	Examples, QR vs linear regression
01d	Outliers	Robustness, RANSAC, Huber, Thiel-Sen
02	Discriminant Analysis	LDA, QDA, Math Foundations, Shrinkage, Estimators
03	Kernel Ridge Regression	KRR vs SVR
04	Support Vector Machines (SVMs)	Classifiers, Regressors, Scoring, Weights, Complexity, Kernels
05	Stochastic Gradient Descent (SGD)	Classifiers, Solvers, Regressors, Sparse Data; Complexity; Stopping/Convergence; Tips
06	K Nearest Neighbors (KNN)	Algos (Ball Tree, KD Tree, Brute Force), Radius-based KNN, Nearest Centroid Classifiers, Caching, Neighborhood Components Analysis (NCA)
07	Gaussian Processes (GPs)	Regressors, Classifiers, Kernels
08	Cross Decomposition	Partial Least Squares (PLS), Canonical PLS, SVD PLS, PLS Regression, Canonical Correlation Analysis (CCA)
09	Naive Bayes (NB)	Gaussian NB, Multinomial NB, Complement NB, Bernoulli NB, Categorical NB, Out-of-core fitting
10	Decision Trees (DTs)	Classifiers, Graphviz, Regressions, Multiple Outputs, Extra Trees, Complexity, Algorithms, Gini, Entropy, Misclassification, Minimal cost-complexity Pruning
11a	Ensembles/Bagging	Methods, Random Forests, Extra Trees, Parameters, Parallel Execution, Feature Importance, Random Tree Embedding
11b	Ensembles/Boosting	Gradient Boosting (GBs), GB Classifiers, GB Regressions, Tree Sizes, Loss Functions, Shrinkage, Subsampling, Feature Importance, Histogram Gradient Boosting (HGB), HGB - Monotonic Constraints
11ba	Ensembles/Boosting/Adaboost	examples
11c	Ensembles/Voting	Hard Voting, Soft Voting, Voting Regressor
11d	Ensembles/General Stacking	Summary
12	Multiclass/Multioutput Problems	Label Binarization, One vs Rest (OvR), One vs One (OvO) Classification, Output Codes, Multilabel, Multioutput Classification, Classifier Chains, Multioutput Regressions, Regression Chains
13	Feature Selection (FS)	Removing Low-Variance Features, Univariate FS,
14	Semi-Supervised	Self-Training Classifier, Label Propagation, Label Spreading
15	Isotonic Regression	Example
16	Calibration Curves	Intro/Example, Cross-Validation, Metrics, Regressors
17	Perceptrons	Intro, Classification, Regression, Regularization, Training, Complexity, Tips
21	Gaussian Mixtures (GMs)	Expectation Maximization, Variational Bayes GM
22	Manifolds	Isomap, Locally Linear Embedding (LLE), Modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), Multidimensional Scaling (MDS), Random Trees Embedding, Spectral Embedding, t-SNE, Neighborhood Components Analysis (NCA)
23	Clustering	K-Means, Voronoi Diagrams, Affinity Propagation, Mean Shift, Spectral Clustering, Agglomerative Clustering, Dendrograms, Connectivity Constraints, Distance Metrics, DBSCAN, Optics, Birch
23a	Clustering Metrics	Rand Index, Mutual Info Score, Homogeneity, Completeness, V-Measure, Fowlkes-Mallows, Silhouette Coefficient, Calinski-Harabasz, Davies-Bouldin, Contingency Matrix, Pair Confusion Matrix
24	Biclustering	Spectral Co-Clustering, Spectral Bi-Clustering, Metrics
25	Component Analysis / Matrix Factorization	PCA, Incremental PCA, PCA w/ Random SVD, PCA w/ Sparse Data, Kernel PCA, Dimension Reduction Comparison, Truncated SVD / LSA, Dictionary Learning, Factor Analysis, Independent Component Analysis, Non-Negative Matrix Factorization (NNMF), Latent Dirichlet Allocation (LDA)
26	Covariance	Empirical CV, Shrunk CV, Max Likelihood Estimation (MLE), Ledoit-Wolf Shrinkage, Oracle Approximating Shrinkage, Sparse Inverse CV, aka Precision Matrix, Mahalanobis Distance
27	Novelties & Outliers	One-Class SVMs, Elliptic Envelope, Isolation Forest, Local Outlier Factor
28	Density Estimation (DE)	Histograms, Kernel DE
29	Restricted Boltzmann Machines (RBMs)	Intro, Training
31	Cross Validation (CV)	Intro, Metrics, Parameter Estimation, Pipelines, Prediction Plots, Nesting, K-Fold, Stratified K-Fold, Leave One Out, Leave P Out, Class Label CV, Grouped Data CV, Predefined Splits, Time Series Splits, Permutation Testing, Visualizations
32	Parameter Tuning	Grid Search, Randomized Optimization, Successive Halving, Composite Estimators & Parameter Spaces, Alternative to Brute Force, Info Criteria (AIC, BIC)
33	Metrics & Scoring (Intro)	scoring, make_scorer
33a	Classification Metrics	Accuracy, Top-K Accuracy, Balanced Accuracy, Cohen's Kappa, Confusion Matrix, Classification Report, Hamming Loss, Precision, Recall, F-Measure, Precision-Recall Curve, Average Precision, Jaccard Similarity, Hinge Loss, Log Loss, Matthews Correlation Coefficient, Receiver Operating Characteristic (ROC) Curves, ROC-AUC, Detection Error Tradeoff (DET), Zero One Loss, Brier Score
33b	Multilabel Ranking Metrics	Coverage Error, Label Ranking Avg Precision (LRAP), Label Ranking Loss, Discounted Cumulative Gain (DCG), Normalized DCG
33c	Regression Metrics	Explained Variance, Max Error, Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Squared Log Error (MSLE), Mean Absolute Pct Error (MAPE), R^2 score, aka Coefficient of Determination , Tweedie Deviances
33d	Dummy Metrics	Dummy Classifiers, Dummy Regressors
34	Viz/Validation	Validation Curve, Learning Curve
41	Viz/Inspection	2D PDPs, 3D PDPs, Individual Conditional Expectation (ICE) Plot
42	Viz/Permutations	Permutation Feature Importance (PFI), Impurity vs Permutation Metrics
50a	Viz/ROC Curves	ROC Curve
50b	Viz/custom PDP Plots	Example
50c	Vis/Classification metrics	Confusion Matrix, ROC Curve, Precision-Recall Curve
61	Composite Transformers	Pipelines, Caching, Regression Target xforms, Feature Unions, Column Transformers
62a	Text Feature Extraction	Bag of Words (BoW), Sparsity, Count Vectorizer, Stop Words, Tf-Idf, Binary Markers, Text file decoding, Hashing Trick, Out-of-core Scaling, Custom Vectorizers
62b	Image Patch Extraction	Extract from Patches, Reconstruct from Patches, Connectivity Graphs
63	Data Preprocessing	Scaling, Quantile Transforms, Power Maps (Box-Cox, Yeo-Johnson), Category Coding, One-Hot Coding, Quantization aka Binning, Feature Binarization
64	Missing Value Imputation	Univariate, Multivariate, Multiple-vs-Single, Nearest-Neighbors, Marking Imputed Values
66	Random Projections	Johnson-Lindenstrauss lemma, Gaussian RP, Sparse RP Empirical Validation
67	Kernel Approximations	Nystroem, RBF Sampler, Additive Chi-Squared Sampler, Skewed Chi-Squared Sampler, Polynomial Sampling - Tensor Sketch
68	Pairwise Ops	Distances vs Kernels, Cosine Similarity, Kernels
69	Transforming Prediction Targets	Label Binarization, Multilabel Binarization, Label Encoding
71	Toy Datasets	Boston, Iris, Diabetes, Digits, Linnerud, Wine, Breast Cancer, Olivetti faces, 20 newsgroups, Labeled faces, Forest covertypes, Reuters corpus, KDD, Cal housing
73	Artificial Data	random-nclass-data, Gaussian blobs, Gaussian quantiles, Circles, Moons, Multilabel class data, Hastie data, BiClusters, Checkerboards, Regression, Friedman1/2/3, S-Curve, Swiss Roll, Low-Rank Matrix, Sparse Coded Signal, Sparse Symmetric Positive Definite (SPD) Matrix
74	Other Data	Sample images, SVMlight/LibSVM formats, OpenML, pandas.io, scipy.io, numpy.routines.io, scikit-image, imageio, scipy.io.wavfile
81	Scaling	Out-of-core ops (BUG = TODO)
82	Latency	Bulk-vs-atomic ops, Latency vs Validation, Latency vs #Features, Latency vs Datatype, Latency vs Feature Extraction, Linear Algebra Libs (BLAS, LAPACK, ATLAS, OpenBLAS, MKL, vecLib)
83	Parallelism	JobLib, OpenMP, NumPy, Oversubscription, config switches
90	Persistence	Pickle, Joblib