Update README.md
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
This commit is contained in:
David Rotermund
GitHub
parent
ccd3235671
commit
d2e506c5c2
1 changed files with 74 additions and 208 deletions
|
|
@ -266,78 +266,49 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.e
|
|||
|
||||
|||
|
||||
|---|---|
|
||||
impute.SimpleImputer(*[, missing_values, ...])
|
||||
Univariate imputer for completing missing values with simple strategies.
|
||||
|
||||
impute.IterativeImputer([estimator, ...])
|
||||
Multivariate imputer that estimates each feature from all the others.
|
||||
|
||||
impute.MissingIndicator(*[, missing_values, ...])
|
||||
Binary indicators for missing values.
|
||||
|
||||
impute.KNNImputer(*[, missing_values, ...])
|
||||
Imputation for completing missing values using k-Nearest Neighbors.
|
||||
|impute.SimpleImputer(*[, missing_values, ...])|Univariate imputer for completing missing values with simple strategies.|
|
||||
|impute.IterativeImputer([estimator, ...])|Multivariate imputer that estimates each feature from all the others.|
|
||||
|impute.MissingIndicator(*[, missing_values, ...])|Binary indicators for missing values.|
|
||||
|impute.KNNImputer(*[, missing_values, ...])|Imputation for completing missing values using k-Nearest Neighbors.|
|
||||
|
||||
## [sklearn.inspection: Inspection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.inspection)
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
inspection.partial_dependence(estimator, X, ...)
|
||||
Partial dependence of features.
|
||||
|
||||
inspection.permutation_importance(estimator, ...)
|
||||
Permutation importance for feature evaluation [Rd9e56ef97513-BRE].
|
||||
|inspection.partial_dependence(estimator, X, ...)|Partial dependence of features.|
|
||||
|inspection.permutation_importance(estimator, ...)|Permutation importance for feature evaluation [Rd9e56ef97513-BRE].|
|
||||
|
||||
### Plotting
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
inspection.DecisionBoundaryDisplay(*, xx0, ...)
|
||||
Decisions boundary visualization.
|
||||
|
||||
inspection.PartialDependenceDisplay(...[, ...])
|
||||
Partial Dependence Plot (PDP).
|
||||
|
||||
|inspection.DecisionBoundaryDisplay(*, xx0, ...)|Decisions boundary visualization.|
|
||||
|inspection.PartialDependenceDisplay(...[, ...])|Partial Dependence Plot (PDP).|
|
||||
|
||||
## [sklearn.isotonic: Isotonic regression](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.isotonic)
|
||||
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
isotonic.IsotonicRegression(*[, y_min, ...])
|
||||
Isotonic regression model.
|
||||
|
||||
isotonic.check_increasing(x, y)
|
||||
Determine whether y is monotonically correlated with x.
|
||||
|
||||
isotonic.isotonic_regression(y, *[, ...])
|
||||
Solve the isotonic regression model.
|
||||
|isotonic.IsotonicRegression(*[, y_min, ...])|Isotonic regression model.|
|
||||
|isotonic.check_increasing(x, y)|Determine whether y is monotonically correlated with x.|
|
||||
|isotonic.isotonic_regression(y, *[, ...])|Solve the isotonic regression model.|
|
||||
|
||||
## [sklearn.kernel_approximation: Kernel Approximation](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_approximation)
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
kernel_approximation.AdditiveChi2Sampler(*)
|
||||
Approximate feature map for additive chi2 kernel.
|
||||
|
||||
kernel_approximation.Nystroem([kernel, ...])
|
||||
Approximate a kernel map using a subset of the training data.
|
||||
|
||||
kernel_approximation.PolynomialCountSketch(*)
|
||||
Polynomial kernel approximation via Tensor Sketch.
|
||||
|
||||
kernel_approximation.RBFSampler(*[, gamma, ...])
|
||||
Approximate a RBF kernel feature map using random Fourier features.
|
||||
|
||||
kernel_approximation.SkewedChi2Sampler(*[, ...])
|
||||
Approximate feature map for "skewed chi-squared" kernel.
|
||||
|kernel_approximation.AdditiveChi2Sampler(*)|Approximate feature map for additive chi2 kernel.|
|
||||
|kernel_approximation.Nystroem([kernel, ...])|Approximate a kernel map using a subset of the training data.|
|
||||
|kernel_approximation.PolynomialCountSketch(*)|Polynomial kernel approximation via Tensor Sketch.|
|
||||
|kernel_approximation.RBFSampler(*[, gamma, ...])|Approximate a RBF kernel feature map using random Fourier features.|
|
||||
|kernel_approximation.SkewedChi2Sampler(*[, ...])|Approximate feature map for "skewed chi-squared" kernel.|
|
||||
|
||||
## [sklearn.kernel_ridge: Kernel Ridge Regression](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_ridge)
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
kernel_ridge.KernelRidge([alpha, kernel, ...])
|
||||
Kernel ridge regression.
|
||||
|kernel_ridge.KernelRidge([alpha, kernel, ...])|Kernel ridge regression.|
|
||||
|
||||
## [sklearn.linear_model: Linear Models](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model)
|
||||
|
||||
|
|
@ -345,198 +316,100 @@ Kernel ridge regression.
|
|||
|
||||
|||
|
||||
|---|---|
|
||||
linear_model.LogisticRegression([penalty, ...])
|
||||
Logistic Regression (aka logit, MaxEnt) classifier.
|
||||
|
||||
linear_model.LogisticRegressionCV(*[, Cs, ...])
|
||||
Logistic Regression CV (aka logit, MaxEnt) classifier.
|
||||
|
||||
linear_model.PassiveAggressiveClassifier(*)
|
||||
Passive Aggressive Classifier.
|
||||
|
||||
linear_model.Perceptron(*[, penalty, alpha, ...])
|
||||
Linear perceptron classifier.
|
||||
|
||||
linear_model.RidgeClassifier([alpha, ...])
|
||||
Classifier using Ridge regression.
|
||||
|
||||
linear_model.RidgeClassifierCV([alphas, ...])
|
||||
Ridge classifier with built-in cross-validation.
|
||||
|
||||
linear_model.SGDClassifier([loss, penalty, ...])
|
||||
Linear classifiers (SVM, logistic regression, etc.) with SGD training.
|
||||
|
||||
linear_model.SGDOneClassSVM([nu, ...])
|
||||
Solves linear One-Class SVM using Stochastic Gradient Descent.
|
||||
|linear_model.LogisticRegression([penalty, ...])|Logistic Regression (aka logit, MaxEnt) classifier.|
|
||||
|linear_model.LogisticRegressionCV(*[, Cs, ...])|Logistic Regression CV (aka logit, MaxEnt) classifier.|
|
||||
|linear_model.PassiveAggressiveClassifier(*)|Passive Aggressive Classifier.|
|
||||
|linear_model.Perceptron(*[, penalty, alpha, ...])|Linear perceptron classifier.|
|
||||
|linear_model.RidgeClassifier([alpha, ...])|Classifier using Ridge regression.|
|
||||
|linear_model.RidgeClassifierCV([alphas, ...])|Ridge classifier with built-in cross-validation.|
|
||||
|linear_model.SGDClassifier([loss, penalty, ...])|Linear classifiers (SVM, logistic regression, etc.) with SGD training.|
|
||||
|linear_model.SGDOneClassSVM([nu, ...])|Solves linear One-Class SVM using Stochastic Gradient Descent.|
|
||||
|
||||
### Classical linear regressors
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
linear_model.LinearRegression(*[, ...])
|
||||
Ordinary least squares Linear Regression.
|
||||
|
||||
linear_model.Ridge([alpha, fit_intercept, ...])
|
||||
Linear least squares with l2 regularization.
|
||||
|
||||
linear_model.RidgeCV([alphas, ...])
|
||||
Ridge regression with built-in cross-validation.
|
||||
|
||||
linear_model.SGDRegressor([loss, penalty, ...])
|
||||
Linear model fitted by minimizing a regularized empirical loss with SGD.
|
||||
|linear_model.LinearRegression(*[, ...])|Ordinary least squares Linear Regression.|
|
||||
|linear_model.Ridge([alpha, fit_intercept, ...])|Linear least squares with l2 regularization.|
|
||||
|linear_model.RidgeCV([alphas, ...])|Ridge regression with built-in cross-validation.|
|
||||
|linear_model.SGDRegressor([loss, penalty, ...])|Linear model fitted by minimizing a regularized empirical loss with SGD.|
|
||||
|
||||
### Regressors with variable selection
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
linear_model.ElasticNet([alpha, l1_ratio, ...])
|
||||
Linear regression with combined L1 and L2 priors as regularizer.
|
||||
|
||||
linear_model.ElasticNetCV(*[, l1_ratio, ...])
|
||||
Elastic Net model with iterative fitting along a regularization path.
|
||||
|
||||
linear_model.Lars(*[, fit_intercept, ...])
|
||||
Least Angle Regression model a.k.a.
|
||||
|
||||
linear_model.LarsCV(*[, fit_intercept, ...])
|
||||
Cross-validated Least Angle Regression model.
|
||||
|
||||
linear_model.Lasso([alpha, fit_intercept, ...])
|
||||
Linear Model trained with L1 prior as regularizer (aka the Lasso).
|
||||
|
||||
linear_model.LassoCV(*[, eps, n_alphas, ...])
|
||||
Lasso linear model with iterative fitting along a regularization path.
|
||||
|
||||
linear_model.LassoLars([alpha, ...])
|
||||
Lasso model fit with Least Angle Regression a.k.a.
|
||||
|
||||
linear_model.LassoLarsCV(*[, fit_intercept, ...])
|
||||
Cross-validated Lasso, using the LARS algorithm.
|
||||
|
||||
linear_model.LassoLarsIC([criterion, ...])
|
||||
Lasso model fit with Lars using BIC or AIC for model selection.
|
||||
|
||||
linear_model.OrthogonalMatchingPursuit(*[, ...])
|
||||
Orthogonal Matching Pursuit model (OMP).
|
||||
|
||||
linear_model.OrthogonalMatchingPursuitCV(*)
|
||||
Cross-validated Orthogonal Matching Pursuit model (OMP).
|
||||
|linear_model.ElasticNet([alpha, l1_ratio, ...])|Linear regression with combined L1 and L2 priors as regularizer.|
|
||||
|linear_model.ElasticNetCV(*[, l1_ratio, ...])|Elastic Net model with iterative fitting along a regularization path.|
|
||||
|linear_model.Lars(*[, fit_intercept, ...])|Least Angle Regression model a.k.a.|
|
||||
|linear_model.LarsCV(*[, fit_intercept, ...])|Cross-validated Least Angle Regression model.|
|
||||
|linear_model.Lasso([alpha, fit_intercept, ...])|Linear Model trained with L1 prior as regularizer (aka the Lasso).|
|
||||
|linear_model.LassoCV(*[, eps, n_alphas, ...])|Lasso linear model with iterative fitting along a regularization path.|
|
||||
|linear_model.LassoLars([alpha, ...])|Lasso model fit with Least Angle Regression a.k.a.|
|
||||
|linear_model.LassoLarsCV(*[, fit_intercept, ...])|Cross-validated Lasso, using the LARS algorithm.|
|
||||
|linear_model.LassoLarsIC([criterion, ...])|Lasso model fit with Lars using BIC or AIC for model selection.|
|
||||
|linear_model.OrthogonalMatchingPursuit(*[, ...])|Orthogonal Matching Pursuit model (OMP).|
|
||||
|linear_model.OrthogonalMatchingPursuitCV(*)|Cross-validated Orthogonal Matching Pursuit model (OMP).|
|
||||
|
||||
### Bayesian regressors
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
linear_model.ARDRegression(*[, max_iter, ...])
|
||||
Bayesian ARD regression.
|
||||
|
||||
linear_model.BayesianRidge(*[, max_iter, ...])
|
||||
Bayesian ridge regression.
|
||||
|linear_model.ARDRegression(*[, max_iter, ...])|Bayesian ARD regression.|
|
||||
|linear_model.BayesianRidge(*[, max_iter, ...])|Bayesian ridge regression.|
|
||||
|
||||
### Multi-task linear regressors with variable selection
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
linear_model.MultiTaskElasticNet([alpha, ...])
|
||||
Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer.
|
||||
|
||||
linear_model.MultiTaskElasticNetCV(*[, ...])
|
||||
Multi-task L1/L2 ElasticNet with built-in cross-validation.
|
||||
|
||||
linear_model.MultiTaskLasso([alpha, ...])
|
||||
Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.
|
||||
|
||||
linear_model.MultiTaskLassoCV(*[, eps, ...])
|
||||
Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.
|
||||
|linear_model.MultiTaskElasticNet([alpha, ...])|Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer.|
|
||||
|linear_model.MultiTaskElasticNetCV(*[, ...])|Multi-task L1/L2 ElasticNet with built-in cross-validation.|
|
||||
|linear_model.MultiTaskLasso([alpha, ...])|Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.|
|
||||
|linear_model.MultiTaskLassoCV(*[, eps, ...])|Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.|
|
||||
|
||||
### Outlier-robust regressors
|
||||
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
linear_model.HuberRegressor(*[, epsilon, ...])
|
||||
L2-regularized linear regression model that is robust to outliers.
|
||||
|
||||
linear_model.QuantileRegressor(*[, ...])
|
||||
Linear regression model that predicts conditional quantiles.
|
||||
|
||||
linear_model.RANSACRegressor([estimator, ...])
|
||||
RANSAC (RANdom SAmple Consensus) algorithm.
|
||||
|
||||
linear_model.TheilSenRegressor(*[, ...])
|
||||
Theil-Sen Estimator: robust multivariate regression model.
|
||||
|linear_model.HuberRegressor(*[, epsilon, ...])|L2-regularized linear regression model that is robust to outliers.|
|
||||
|linear_model.QuantileRegressor(*[, ...])|Linear regression model that predicts conditional quantiles.|
|
||||
|linear_model.RANSACRegressor([estimator, ...])|RANSAC (RANdom SAmple Consensus) algorithm.|
|
||||
|linear_model.TheilSenRegressor(*[, ...])|Theil-Sen Estimator: robust multivariate regression model.|
|
||||
|
||||
### Generalized linear models (GLM) for regression
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
linear_model.PoissonRegressor(*[, alpha, ...])
|
||||
Generalized Linear Model with a Poisson distribution.
|
||||
|
||||
linear_model.TweedieRegressor(*[, power, ...])
|
||||
Generalized Linear Model with a Tweedie distribution.
|
||||
|
||||
linear_model.GammaRegressor(*[, alpha, ...])
|
||||
Generalized Linear Model with a Gamma distribution.
|
||||
|linear_model.PoissonRegressor(*[, alpha, ...])|Generalized Linear Model with a Poisson distribution.|
|
||||
|linear_model.TweedieRegressor(*[, power, ...])|Generalized Linear Model with a Tweedie distribution.|
|
||||
|linear_model.GammaRegressor(*[, alpha, ...])|Generalized Linear Model with a Gamma distribution.|
|
||||
|
||||
### Miscellaneous
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
linear_model.PassiveAggressiveRegressor(*[, ...])
|
||||
Passive Aggressive Regressor.
|
||||
|
||||
linear_model.enet_path(X, y, *[, l1_ratio, ...])
|
||||
Compute elastic net path with coordinate descent.
|
||||
|
||||
linear_model.lars_path(X, y[, Xy, Gram, ...])
|
||||
Compute Least Angle Regression or Lasso path using the LARS algorithm [1].
|
||||
|
||||
linear_model.lars_path_gram(Xy, Gram, *, ...)
|
||||
The lars_path in the sufficient stats mode [1].
|
||||
|
||||
linear_model.lasso_path(X, y, *[, eps, ...])
|
||||
Compute Lasso path with coordinate descent.
|
||||
|
||||
linear_model.orthogonal_mp(X, y, *[, ...])
|
||||
Orthogonal Matching Pursuit (OMP).
|
||||
|
||||
linear_model.orthogonal_mp_gram(Gram, Xy, *)
|
||||
Gram Orthogonal Matching Pursuit (OMP).
|
||||
|
||||
linear_model.ridge_regression(X, y, alpha, *)
|
||||
Solve the ridge equation by the method of normal equations.
|
||||
|linear_model.PassiveAggressiveRegressor(*[, ...])|Passive Aggressive Regressor.|
|
||||
|linear_model.enet_path(X, y, *[, l1_ratio, ...])|Compute elastic net path with coordinate descent.|
|
||||
|linear_model.lars_path(X, y[, Xy, Gram, ...])|Compute Least Angle Regression or Lasso path using the LARS algorithm [1].|
|
||||
|linear_model.lars_path_gram(Xy, Gram, *, ...)|The lars_path in the sufficient stats mode [1].|
|
||||
|linear_model.lasso_path(X, y, *[, eps, ...])|Compute Lasso path with coordinate descent.|
|
||||
|linear_model.orthogonal_mp(X, y, *[, ...])|Orthogonal Matching Pursuit (OMP).|
|
||||
|linear_model.orthogonal_mp_gram(Gram, Xy, *)|Gram Orthogonal Matching Pursuit (OMP).|
|
||||
|linear_model.ridge_regression(X, y, alpha, *)|Solve the ridge equation by the method of normal equations.|
|
||||
|
||||
## [sklearn.manifold: Manifold Learning](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.manifold)
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
manifold.Isomap(*[, n_neighbors, radius, ...])
|
||||
Isomap Embedding.
|
||||
|
||||
manifold.LocallyLinearEmbedding(*[, ...])
|
||||
Locally Linear Embedding.
|
||||
|
||||
manifold.MDS([n_components, metric, n_init, ...])
|
||||
Multidimensional scaling.
|
||||
|
||||
manifold.SpectralEmbedding([n_components, ...])
|
||||
Spectral embedding for non-linear dimensionality reduction.
|
||||
|
||||
manifold.TSNE([n_components, perplexity, ...])
|
||||
T-distributed Stochastic Neighbor Embedding.
|
||||
|
||||
manifold.locally_linear_embedding(X, *, ...)
|
||||
Perform a Locally Linear Embedding analysis on the data.
|
||||
|
||||
manifold.smacof(dissimilarities, *[, ...])
|
||||
Compute multidimensional scaling using the SMACOF algorithm.
|
||||
|
||||
manifold.spectral_embedding(adjacency, *[, ...])
|
||||
Project the sample on the first eigenvectors of the graph Laplacian.
|
||||
|
||||
manifold.trustworthiness(X, X_embedded, *[, ...])
|
||||
Indicate to what extent the local structure is retained.
|
||||
|
||||
|manifold.Isomap(*[, n_neighbors, radius, ...])|Isomap Embedding.|
|
||||
|manifold.LocallyLinearEmbedding(*[, ...])|Locally Linear Embedding.|
|
||||
|manifold.MDS([n_components, metric, n_init, ...])|Multidimensional scaling.|
|
||||
|manifold.SpectralEmbedding([n_components, ...])|Spectral embedding for non-linear dimensionality reduction.|
|
||||
|manifold.TSNE([n_components, perplexity, ...])|T-distributed Stochastic Neighbor Embedding.|
|
||||
|manifold.locally_linear_embedding(X, *, ...)|Perform a Locally Linear Embedding analysis on the data.|
|
||||
|manifold.smacof(dissimilarities, *[, ...])|Compute multidimensional scaling using the SMACOF algorithm.|
|
||||
|manifold.spectral_embedding(adjacency, *[, ...])|Project the sample on the first eigenvectors of the graph Laplacian.|
|
||||
|manifold.trustworthiness(X, X_embedded, *[, ...])|Indicate to what extent the local structure is retained.|
|
||||
|
||||
|
||||
## [sklearn.metrics: Metrics](https://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics)
|
||||
|
|
@ -545,17 +418,10 @@ Indicate to what extent the local structure is retained.
|
|||
|
||||
|||
|
||||
|---|---|
|
||||
metrics.check_scoring(estimator[, scoring, ...])
|
||||
Determine scorer from user options.
|
||||
|
||||
metrics.get_scorer(scoring)
|
||||
Get a scorer from string.
|
||||
|
||||
metrics.get_scorer_names()
|
||||
Get the names of all available scorers.
|
||||
|
||||
metrics.make_scorer(score_func, *[, ...])
|
||||
Make a scorer from a performance metric or loss function.
|
||||
|metrics.check_scoring(estimator[, scoring, ...])|Determine scorer from user options.|
|
||||
|metrics.get_scorer(scoring)|Get a scorer from string.|
|
||||
|metrics.get_scorer_names()|Get the names of all available scorers.|
|
||||
|metrics.make_scorer(score_func, *[, ...])|Make a scorer from a performance metric or loss function.|
|
||||
|
||||
### Classification metrics
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue