15 KiB
Overview
{:.no_toc}
* TOC {:toc}The goal
scikit-learn is a machine learning tool kit for data analysis.
Questions to David Rotermund
pip install scikit-learn
- Simple and efficient tools for predictive data analysis
- Accessible to everybody, and reusable in various contexts
- Built on NumPy, SciPy, and matplotlib
I will keep it short and I will mark the most relevant tools in bold
sklearn.base: Base classes and utility functions
see here
sklearn.calibration: Probability Calibration
calibration.CalibratedClassifierCV([...]) Probability calibration with isotonic regression or logistic regression.
calibration.calibration_curve(y_true, y_prob, *) Compute true and predicted probabilities for a calibration curve.
sklearn.cluster: Clustering
see more here
cluster.AffinityPropagation(*[, damping, ...]) Perform Affinity Propagation Clustering of data.
cluster.AgglomerativeClustering([...]) Agglomerative Clustering.
cluster.Birch(*[, threshold, ...]) Implements the BIRCH clustering algorithm.
cluster.DBSCAN([eps, min_samples, metric, ...]) Perform DBSCAN clustering from vector array or distance matrix.
cluster.HDBSCAN([min_cluster_size, ...]) Cluster data using hierarchical density-based clustering.
cluster.FeatureAgglomeration([n_clusters, ...]) Agglomerate features.
cluster.KMeans([n_clusters, init, n_init, ...]) K-Means clustering.
cluster.BisectingKMeans([n_clusters, init, ...]) Bisecting K-Means clustering.
cluster.MiniBatchKMeans([n_clusters, init, ...]) Mini-Batch K-Means clustering.
cluster.MeanShift(*[, bandwidth, seeds, ...]) Mean shift clustering using a flat kernel.
cluster.OPTICS(*[, min_samples, max_eps, ...]) Estimate clustering structure from vector array.
cluster.SpectralClustering([n_clusters, ...]) Apply clustering to a projection of the normalized Laplacian.
cluster.SpectralBiclustering([n_clusters, ...]) Spectral biclustering (Kluger, 2003).
cluster.SpectralCoclustering([n_clusters, ...]) Spectral Co-Clustering algorithm (Dhillon, 2001).
sklearn.compose: Composite Estimators
compose.ColumnTransformer(transformers, *[, ...]) Applies transformers to columns of an array or pandas DataFrame.
compose.TransformedTargetRegressor([...]) Meta-estimator to regress on a transformed target.
compose.make_column_transformer(*transformers) Construct a ColumnTransformer from the given transformers.
compose.make_column_selector([pattern, ...]) Create a callable to select columns to be used with ColumnTransformer.
sklearn.covariance: Covariance Estimators
covariance.EmpiricalCovariance(*[, ...]) Maximum likelihood covariance estimator.
covariance.EllipticEnvelope(*[, ...]) An object for detecting outliers in a Gaussian distributed dataset.
covariance.GraphicalLasso([alpha, mode, ...]) Sparse inverse covariance estimation with an l1-penalized estimator.
covariance.GraphicalLassoCV(*[, alphas, ...]) Sparse inverse covariance w/ cross-validated choice of the l1 penalty.
covariance.LedoitWolf(*[, store_precision, ...]) LedoitWolf Estimator.
covariance.MinCovDet(*[, store_precision, ...]) Minimum Covariance Determinant (MCD): robust estimator of covariance.
covariance.OAS(*[, store_precision, ...]) Oracle Approximating Shrinkage Estimator as proposed in [R69773891e6a6-1].
covariance.ShrunkCovariance(*[, ...]) Covariance estimator with shrinkage.
covariance.empirical_covariance(X, *[, ...]) Compute the Maximum likelihood covariance estimator.
covariance.graphical_lasso(emp_cov, alpha, *) L1-penalized covariance estimator.
covariance.ledoit_wolf(X, *[, ...]) Estimate the shrunk Ledoit-Wolf covariance matrix.
covariance.ledoit_wolf_shrinkage(X[, ...]) Estimate the shrunk Ledoit-Wolf covariance matrix.
covariance.oas(X, *[, assume_centered]) Estimate covariance with the Oracle Approximating Shrinkage as proposed in [Rca3a42e5ec35-1].
covariance.shrunk_covariance(emp_cov[, ...]) Calculate a covariance matrix shrunk on the diagonal.
sklearn.cross_decomposition: Cross decomposition
cross_decomposition.CCA([n_components, ...]) Canonical Correlation Analysis, also known as "Mode B" PLS.
cross_decomposition.PLSCanonical([...]) Partial Least Squares transformer and regressor.
cross_decomposition.PLSRegression([...]) PLS regression.
cross_decomposition.PLSSVD([n_components, ...]) Partial Least Square SVD.
sklearn.datasets: Datasets
see here
sklearn.decomposition: Matrix Decomposition
decomposition.DictionaryLearning([...]) Dictionary learning.
decomposition.FactorAnalysis([n_components, ...]) Factor Analysis (FA).
decomposition.FastICA([n_components, ...]) FastICA: a fast algorithm for Independent Component Analysis.
decomposition.IncrementalPCA([n_components, ...]) Incremental principal components analysis (IPCA).
decomposition.KernelPCA([n_components, ...]) Kernel Principal component analysis (KPCA) [R396fc7d924b8-1].
decomposition.LatentDirichletAllocation([...]) Latent Dirichlet Allocation with online variational Bayes algorithm.
decomposition.MiniBatchDictionaryLearning([...]) Mini-batch dictionary learning.
decomposition.MiniBatchSparsePCA([...]) Mini-batch Sparse Principal Components Analysis.
decomposition.NMF([n_components, init, ...]) Non-Negative Matrix Factorization (NMF).
decomposition.MiniBatchNMF([n_components, ...]) Mini-Batch Non-Negative Matrix Factorization (NMF).
decomposition.PCA([n_components, copy, ...]) Principal component analysis (PCA).
decomposition.SparsePCA([n_components, ...]) Sparse Principal Components Analysis (SparsePCA).
decomposition.SparseCoder(dictionary, *[, ...]) Sparse coding.
decomposition.TruncatedSVD([n_components, ...]) Dimensionality reduction using truncated SVD (aka LSA).
decomposition.dict_learning(X, n_components, ...) Solve a dictionary learning matrix factorization problem.
decomposition.dict_learning_online(X[, ...]) Solve a dictionary learning matrix factorization problem online.
decomposition.fastica(X[, n_components, ...]) Perform Fast Independent Component Analysis.
decomposition.non_negative_factorization(X) Compute Non-negative Matrix Factorization (NMF).
decomposition.sparse_encode(X, dictionary, *) Sparse coding.
sklearn.discriminant_analysis: Discriminant Analysis
discriminant_analysis.LinearDiscriminantAnalysis([...]) Linear Discriminant Analysis.
discriminant_analysis.QuadraticDiscriminantAnalysis(*) Quadratic Discriminant Analysis.
sklearn.dummy: Dummy estimators
dummy.DummyClassifier(*[, strategy, ...]) DummyClassifier makes predictions that ignore the input features.
dummy.DummyRegressor(*[, strategy, ...]) Regressor that makes predictions using simple rules.
sklearn.ensemble: Ensemble Methods
ensemble.AdaBoostClassifier([estimator, ...]) An AdaBoost classifier.
ensemble.AdaBoostRegressor([estimator, ...]) An AdaBoost regressor.
ensemble.BaggingClassifier([estimator, ...]) A Bagging classifier.
ensemble.BaggingRegressor([estimator, ...]) A Bagging regressor.
ensemble.ExtraTreesClassifier([...]) An extra-trees classifier.
ensemble.ExtraTreesRegressor([n_estimators, ...]) An extra-trees regressor.
ensemble.GradientBoostingClassifier(*[, ...]) Gradient Boosting for classification.
ensemble.GradientBoostingRegressor(*[, ...]) Gradient Boosting for regression.
ensemble.IsolationForest(*[, n_estimators, ...]) Isolation Forest Algorithm.
ensemble.RandomForestClassifier([...]) A random forest classifier.
ensemble.RandomForestRegressor([...]) A random forest regressor.
ensemble.RandomTreesEmbedding([...]) An ensemble of totally random trees.
ensemble.StackingClassifier(estimators[, ...]) Stack of estimators with a final classifier.
ensemble.StackingRegressor(estimators[, ...]) Stack of estimators with a final regressor.
ensemble.VotingClassifier(estimators, *[, ...]) Soft Voting/Majority Rule classifier for unfitted estimators.
ensemble.VotingRegressor(estimators, *[, ...]) Prediction voting regressor for unfitted estimators.
ensemble.HistGradientBoostingRegressor([...]) Histogram-based Gradient Boosting Regression Tree.
ensemble.HistGradientBoostingClassifier([...]) Histogram-based Gradient Boosting Classification Tree.
sklearn.exceptions: Exceptions and warnings
see here
sklearn.experimental: Experimental
see here
sklearn.preprocessing: Preprocessing and Normalization
preprocessing.Binarizer(*[, threshold, copy]) Binarize data (set feature values to 0 or 1) according to a threshold.
preprocessing.FunctionTransformer([func, ...]) Constructs a transformer from an arbitrary callable.
preprocessing.KBinsDiscretizer([n_bins, ...]) Bin continuous data into intervals.
preprocessing.KernelCenterer() Center an arbitrary kernel matrix
preprocessing.LabelBinarizer(*[, neg_label, ...]) Binarize labels in a one-vs-all fashion.
preprocessing.LabelEncoder() Encode target labels with value between 0 and n_classes-1.
preprocessing.MultiLabelBinarizer(*[, ...]) Transform between iterable of iterables and a multilabel format.
preprocessing.MaxAbsScaler(*[, copy]) Scale each feature by its maximum absolute value.
preprocessing.MinMaxScaler([feature_range, ...]) Transform features by scaling each feature to a given range.
preprocessing.Normalizer([norm, copy]) Normalize samples individually to unit norm.
preprocessing.OneHotEncoder(*[, categories, ...]) Encode categorical features as a one-hot numeric array.
preprocessing.OrdinalEncoder(*[, ...]) Encode categorical features as an integer array.
preprocessing.PolynomialFeatures([degree, ...]) Generate polynomial and interaction features.
preprocessing.PowerTransformer([method, ...]) Apply a power transform featurewise to make data more Gaussian-like.
preprocessing.QuantileTransformer(*[, ...]) Transform features using quantiles information.
preprocessing.RobustScaler(*[, ...]) Scale features using statistics that are robust to outliers.
preprocessing.SplineTransformer([n_knots, ...]) Generate univariate B-spline bases for features.
preprocessing.StandardScaler(*[, copy, ...]) Standardize features by removing the mean and scaling to unit variance.
preprocessing.TargetEncoder([categories, ...]) Target Encoder for regression and classification targets.
preprocessing.add_dummy_feature(X[, value]) Augment dataset with an additional dummy feature.
preprocessing.binarize(X, *[, threshold, copy]) Boolean thresholding of array-like or scipy.sparse matrix.
preprocessing.label_binarize(y, *, classes) Binarize labels in a one-vs-all fashion.
preprocessing.maxabs_scale(X, *[, axis, copy]) Scale each feature to the [-1, 1] range without breaking the sparsity.
preprocessing.minmax_scale(X[, ...]) Transform features by scaling each feature to a given range.
preprocessing.normalize(X[, norm, axis, ...]) Scale input vectors individually to unit norm (vector length).
preprocessing.quantile_transform(X, *[, ...]) Transform features using quantiles information.
preprocessing.robust_scale(X, *[, axis, ...]) Standardize a dataset along any axis.
preprocessing.scale(X, *[, axis, with_mean, ...]) Standardize a dataset along any axis.
preprocessing.power_transform(X[, method, ...]) Parametric, monotonic transformation to make data more Gaussian-like.
sklearn.random_projection: Random projection
random_projection.GaussianRandomProjection([...]) Reduce dimensionality through Gaussian random projection.
random_projection.SparseRandomProjection([...]) Reduce dimensionality through sparse random projection.
random_projection.johnson_lindenstrauss_min_dim(...) Find a 'safe' number of components to randomly project to.
sklearn.semi_supervised: Semi-Supervised Learning
semi_supervised.LabelPropagation([kernel, ...]) Label Propagation classifier.
semi_supervised.LabelSpreading([kernel, ...]) LabelSpreading model for semi-supervised learning.
semi_supervised.SelfTrainingClassifier(...) Self-training classifier.
sklearn.svm: Support Vector Machines
svm.LinearSVC([penalty, loss, dual, tol, C, ...]) Linear Support Vector Classification.
svm.LinearSVR(*[, epsilon, tol, C, loss, ...]) Linear Support Vector Regression.
svm.NuSVC(*[, nu, kernel, degree, gamma, ...]) Nu-Support Vector Classification.
svm.NuSVR(*[, nu, C, kernel, degree, gamma, ...]) Nu Support Vector Regression.
svm.OneClassSVM(*[, kernel, degree, gamma, ...]) Unsupervised Outlier Detection.
svm.SVC(*[, C, kernel, degree, gamma, ...]) C-Support Vector Classification.
svm.SVR(*[, kernel, degree, gamma, coef0, ...]) Epsilon-Support Vector Regression.
svm.l1_min_c(X, y, *[, loss, fit_intercept, ...]) Return the lowest bound for C.
sklearn.tree: Decision Trees
tree.DecisionTreeClassifier(*[, criterion, ...]) A decision tree classifier.
tree.DecisionTreeRegressor(*[, criterion, ...]) A decision tree regressor.
tree.ExtraTreeClassifier(*[, criterion, ...]) An extremely randomized tree classifier.
tree.ExtraTreeRegressor(*[, criterion, ...]) An extremely randomized tree regressor.
tree.export_graphviz(decision_tree[, ...]) Export a decision tree in DOT format.
tree.export_text(decision_tree, *[, ...]) Build a text report showing the rules of a decision tree.
tree.plot_tree(decision_tree, *[, ...]) Plot a decision tree.
sklearn.utils: Utilities
see here