You are viewing documentation about an older version (1.2.0). View latest version

snowflake.ml.modeling

snowflake.ml.modeling.calibration

Classes

CalibratedClassifierCV(*[, estimator, ...])

Probability calibration with isotonic regression or logistic regression For more details on this class, see sklearn.calibration.CalibratedClassifierCV

snowflake.ml.modeling.cluster

Classes

AffinityPropagation(*[, damping, max_iter, ...])

Perform Affinity Propagation Clustering of data For more details on this class, see sklearn.cluster.AffinityPropagation

AgglomerativeClustering(*[, n_clusters, ...])

Agglomerative Clustering For more details on this class, see sklearn.cluster.AgglomerativeClustering

Birch(*[, threshold, branching_factor, ...])

Implements the BIRCH clustering algorithm For more details on this class, see sklearn.cluster.Birch

BisectingKMeans(*[, n_clusters, init, ...])

Bisecting K-Means clustering For more details on this class, see sklearn.cluster.BisectingKMeans

DBSCAN(*[, eps, min_samples, metric, ...])

Perform DBSCAN clustering from vector array or distance matrix For more details on this class, see sklearn.cluster.DBSCAN

FeatureAgglomeration(*[, n_clusters, ...])

Agglomerate features For more details on this class, see sklearn.cluster.FeatureAgglomeration

KMeans(*[, n_clusters, init, n_init, ...])

K-Means clustering For more details on this class, see sklearn.cluster.KMeans

MeanShift(*[, bandwidth, seeds, ...])

Mean shift clustering using a flat kernel For more details on this class, see sklearn.cluster.MeanShift

MiniBatchKMeans(*[, n_clusters, init, ...])

Mini-Batch K-Means clustering For more details on this class, see sklearn.cluster.MiniBatchKMeans

OPTICS(*[, min_samples, max_eps, metric, p, ...])

Estimate clustering structure from vector array For more details on this class, see sklearn.cluster.OPTICS

SpectralBiclustering(*[, n_clusters, ...])

Spectral biclustering (Kluger, 2003) For more details on this class, see sklearn.cluster.SpectralBiclustering

SpectralClustering(*[, n_clusters, ...])

Apply clustering to a projection of the normalized Laplacian For more details on this class, see sklearn.cluster.SpectralClustering

SpectralCoclustering(*[, n_clusters, ...])

Spectral Co-Clustering algorithm (Dhillon, 2001) For more details on this class, see sklearn.cluster.SpectralCoclustering

snowflake.ml.modeling.compose

Classes

ColumnTransformer(*, transformers[, ...])

Applies transformers to columns of an array or pandas DataFrame For more details on this class, see sklearn.compose.ColumnTransformer

TransformedTargetRegressor(*[, regressor, ...])

Meta-estimator to regress on a transformed target For more details on this class, see sklearn.compose.TransformedTargetRegressor

snowflake.ml.modeling.covariance

Classes

EllipticEnvelope(*[, store_precision, ...])

An object for detecting outliers in a Gaussian distributed dataset For more details on this class, see sklearn.covariance.EllipticEnvelope

EmpiricalCovariance(*[, store_precision, ...])

Maximum likelihood covariance estimator For more details on this class, see sklearn.covariance.EmpiricalCovariance

GraphicalLasso(*[, alpha, mode, covariance, ...])

Sparse inverse covariance estimation with an l1-penalized estimator For more details on this class, see sklearn.covariance.GraphicalLasso

GraphicalLassoCV(*[, alphas, n_refinements, ...])

Sparse inverse covariance w/ cross-validated choice of the l1 penalty For more details on this class, see sklearn.covariance.GraphicalLassoCV

LedoitWolf(*[, store_precision, ...])

LedoitWolf Estimator For more details on this class, see sklearn.covariance.LedoitWolf

MinCovDet(*[, store_precision, ...])

Minimum Covariance Determinant (MCD): robust estimator of covariance For more details on this class, see sklearn.covariance.MinCovDet

OAS(*[, store_precision, assume_centered, ...])

Oracle Approximating Shrinkage Estimator as proposed in [1]_ For more details on this class, see sklearn.covariance.OAS

ShrunkCovariance(*[, store_precision, ...])

Covariance estimator with shrinkage For more details on this class, see sklearn.covariance.ShrunkCovariance

snowflake.ml.modeling.decomposition

Classes

DictionaryLearning(*[, n_components, alpha, ...])

Dictionary learning For more details on this class, see sklearn.decomposition.DictionaryLearning

FactorAnalysis(*[, n_components, tol, copy, ...])

Factor Analysis (FA) For more details on this class, see sklearn.decomposition.FactorAnalysis

FastICA(*[, n_components, algorithm, ...])

FastICA: a fast algorithm for Independent Component Analysis For more details on this class, see sklearn.decomposition.FastICA

IncrementalPCA(*[, n_components, whiten, ...])

Incremental principal components analysis (IPCA) For more details on this class, see sklearn.decomposition.IncrementalPCA

KernelPCA(*[, n_components, kernel, gamma, ...])

Kernel Principal component analysis (KPCA) [1]_ For more details on this class, see sklearn.decomposition.KernelPCA

MiniBatchDictionaryLearning(*[, ...])

Mini-batch dictionary learning For more details on this class, see sklearn.decomposition.MiniBatchDictionaryLearning

MiniBatchSparsePCA(*[, n_components, alpha, ...])

Mini-batch Sparse Principal Components Analysis For more details on this class, see sklearn.decomposition.MiniBatchSparsePCA

PCA(*[, n_components, copy, whiten, ...])

Principal component analysis (PCA) For more details on this class, see sklearn.decomposition.PCA

SparsePCA(*[, n_components, alpha, ...])

Sparse Principal Components Analysis (SparsePCA) For more details on this class, see sklearn.decomposition.SparsePCA

TruncatedSVD(*[, n_components, algorithm, ...])

Dimensionality reduction using truncated SVD (aka LSA) For more details on this class, see sklearn.decomposition.TruncatedSVD

snowflake.ml.modeling.discriminant_analysis

Classes

LinearDiscriminantAnalysis(*[, solver, ...])

Linear Discriminant Analysis For more details on this class, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis

QuadraticDiscriminantAnalysis(*[, priors, ...])

Quadratic Discriminant Analysis For more details on this class, see sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis

snowflake.ml.modeling.ensemble

Classes

AdaBoostClassifier(*[, estimator, ...])

An AdaBoost classifier For more details on this class, see sklearn.ensemble.AdaBoostClassifier

AdaBoostRegressor(*[, estimator, ...])

An AdaBoost regressor For more details on this class, see sklearn.ensemble.AdaBoostRegressor

BaggingClassifier(*[, estimator, ...])

A Bagging classifier For more details on this class, see sklearn.ensemble.BaggingClassifier

BaggingRegressor(*[, estimator, ...])

A Bagging regressor For more details on this class, see sklearn.ensemble.BaggingRegressor

ExtraTreesClassifier(*[, n_estimators, ...])

An extra-trees classifier For more details on this class, see sklearn.ensemble.ExtraTreesClassifier

ExtraTreesRegressor(*[, n_estimators, ...])

An extra-trees regressor For more details on this class, see sklearn.ensemble.ExtraTreesRegressor

GradientBoostingClassifier(*[, loss, ...])

Gradient Boosting for classification For more details on this class, see sklearn.ensemble.GradientBoostingClassifier

GradientBoostingRegressor(*[, loss, ...])

Gradient Boosting for regression For more details on this class, see sklearn.ensemble.GradientBoostingRegressor

HistGradientBoostingClassifier(*[, loss, ...])

Histogram-based Gradient Boosting Classification Tree For more details on this class, see sklearn.ensemble.HistGradientBoostingClassifier

HistGradientBoostingRegressor(*[, loss, ...])

Histogram-based Gradient Boosting Regression Tree For more details on this class, see sklearn.ensemble.HistGradientBoostingRegressor

IsolationForest(*[, n_estimators, ...])

Isolation Forest Algorithm For more details on this class, see sklearn.ensemble.IsolationForest

RandomForestClassifier(*[, n_estimators, ...])

A random forest classifier For more details on this class, see sklearn.ensemble.RandomForestClassifier

RandomForestRegressor(*[, n_estimators, ...])

A random forest regressor For more details on this class, see sklearn.ensemble.RandomForestRegressor

StackingRegressor(*, estimators[, ...])

Stack of estimators with a final regressor For more details on this class, see sklearn.ensemble.StackingRegressor

VotingClassifier(*, estimators[, voting, ...])

Soft Voting/Majority Rule classifier for unfitted estimators For more details on this class, see sklearn.ensemble.VotingClassifier

VotingRegressor(*, estimators[, weights, ...])

Prediction voting regressor for unfitted estimators For more details on this class, see sklearn.ensemble.VotingRegressor

snowflake.ml.modeling.feature_selection

Classes

GenericUnivariateSelect(*[, score_func, ...])

Univariate feature selector with configurable strategy For more details on this class, see sklearn.feature_selection.GenericUnivariateSelect

SelectFdr(*[, score_func, alpha])

Filter: Select the p-values for an estimated false discovery rate For more details on this class, see sklearn.feature_selection.SelectFdr

SelectFpr(*[, score_func, alpha])

Filter: Select the pvalues below alpha based on a FPR test For more details on this class, see sklearn.feature_selection.SelectFpr

SelectFwe(*[, score_func, alpha])

Filter: Select the p-values corresponding to Family-wise error rate For more details on this class, see sklearn.feature_selection.SelectFwe

SelectKBest(*[, score_func, k])

Select features according to the k highest scores For more details on this class, see sklearn.feature_selection.SelectKBest

SelectPercentile(*[, score_func, percentile])

Select features according to a percentile of the highest scores For more details on this class, see sklearn.feature_selection.SelectPercentile

SequentialFeatureSelector(*, estimator[, ...])

Transformer that performs Sequential Feature Selection For more details on this class, see sklearn.feature_selection.SequentialFeatureSelector

VarianceThreshold(*[, threshold, ...])

Feature selector that removes all low-variance features For more details on this class, see sklearn.feature_selection.VarianceThreshold

snowflake.ml.modeling.gaussian_process

Classes

GaussianProcessClassifier(*[, kernel, ...])

Gaussian process classification (GPC) based on Laplace approximation For more details on this class, see sklearn.gaussian_process.GaussianProcessClassifier

GaussianProcessRegressor(*[, kernel, alpha, ...])

Gaussian process regression (GPR) For more details on this class, see sklearn.gaussian_process.GaussianProcessRegressor

snowflake.ml.modeling.impute

Classes

IterativeImputer(*[, estimator, ...])

Multivariate imputer that estimates each feature from all the others For more details on this class, see sklearn.impute.IterativeImputer

KNNImputer(*[, missing_values, n_neighbors, ...])

Imputation for completing missing values using k-Nearest Neighbors For more details on this class, see sklearn.impute.KNNImputer

MissingIndicator(*[, missing_values, ...])

Binary indicators for missing values For more details on this class, see sklearn.impute.MissingIndicator

SimpleImputer(*[, missing_values, strategy, ...])

Univariate imputer for completing missing values with simple strategies.

snowflake.ml.modeling.kernel_approximation

Classes

AdditiveChi2Sampler(*[, sample_steps, ...])

Approximate feature map for additive chi2 kernel For more details on this class, see sklearn.kernel_approximation.AdditiveChi2Sampler

Nystroem(*[, kernel, gamma, coef0, degree, ...])

Approximate a kernel map using a subset of the training data For more details on this class, see sklearn.kernel_approximation.Nystroem

PolynomialCountSketch(*[, gamma, degree, ...])

Polynomial kernel approximation via Tensor Sketch For more details on this class, see sklearn.kernel_approximation.PolynomialCountSketch

RBFSampler(*[, gamma, n_components, ...])

Approximate a RBF kernel feature map using random Fourier features For more details on this class, see sklearn.kernel_approximation.RBFSampler

SkewedChi2Sampler(*[, skewedness, ...])

Approximate feature map for "skewed chi-squared" kernel For more details on this class, see sklearn.kernel_approximation.SkewedChi2Sampler

snowflake.ml.modeling.kernel_ridge

Classes

KernelRidge(*[, alpha, kernel, gamma, ...])

Kernel ridge regression For more details on this class, see sklearn.kernel_ridge.KernelRidge

snowflake.ml.modeling.lightgbm

Classes

LGBMClassifier(*[, boosting_type, ...])

LightGBM classifier For more details on this class, see lightgbm.LGBMClassifier

LGBMRegressor(*[, boosting_type, ...])

LightGBM regressor For more details on this class, see lightgbm.LGBMRegressor

snowflake.ml.modeling.linear_model

Classes

ARDRegression(*[, max_iter, tol, alpha_1, ...])

Bayesian ARD regression For more details on this class, see sklearn.linear_model.ARDRegression

BayesianRidge(*[, max_iter, tol, alpha_1, ...])

Bayesian ridge regression For more details on this class, see sklearn.linear_model.BayesianRidge

ElasticNet(*[, alpha, l1_ratio, ...])

Linear regression with combined L1 and L2 priors as regularizer For more details on this class, see sklearn.linear_model.ElasticNet

ElasticNetCV(*[, l1_ratio, eps, n_alphas, ...])

Elastic Net model with iterative fitting along a regularization path For more details on this class, see sklearn.linear_model.ElasticNetCV

GammaRegressor(*[, alpha, fit_intercept, ...])

Generalized Linear Model with a Gamma distribution For more details on this class, see sklearn.linear_model.GammaRegressor

HuberRegressor(*[, epsilon, max_iter, ...])

L2-regularized linear regression model that is robust to outliers For more details on this class, see sklearn.linear_model.HuberRegressor

Lars(*[, fit_intercept, verbose, normalize, ...])

Least Angle Regression model a For more details on this class, see sklearn.linear_model.Lars

LarsCV(*[, fit_intercept, verbose, ...])

Cross-validated Least Angle Regression model For more details on this class, see sklearn.linear_model.LarsCV

Lasso(*[, alpha, fit_intercept, precompute, ...])

Linear Model trained with L1 prior as regularizer (aka the Lasso) For more details on this class, see sklearn.linear_model.Lasso

LassoCV(*[, eps, n_alphas, alphas, ...])

Lasso linear model with iterative fitting along a regularization path For more details on this class, see sklearn.linear_model.LassoCV

LassoLars(*[, alpha, fit_intercept, ...])

Lasso model fit with Least Angle Regression a For more details on this class, see sklearn.linear_model.LassoLars

LassoLarsCV(*[, fit_intercept, verbose, ...])

Cross-validated Lasso, using the LARS algorithm For more details on this class, see sklearn.linear_model.LassoLarsCV

LassoLarsIC(*[, criterion, fit_intercept, ...])

Lasso model fit with Lars using BIC or AIC for model selection For more details on this class, see sklearn.linear_model.LassoLarsIC

LinearRegression(*[, fit_intercept, copy_X, ...])

Ordinary least squares Linear Regression For more details on this class, see sklearn.linear_model.LinearRegression

LogisticRegression(*[, penalty, dual, tol, ...])

Logistic Regression (aka logit, MaxEnt) classifier For more details on this class, see sklearn.linear_model.LogisticRegression

LogisticRegressionCV(*[, Cs, fit_intercept, ...])

Logistic Regression CV (aka logit, MaxEnt) classifier For more details on this class, see sklearn.linear_model.LogisticRegressionCV

MultiTaskElasticNet(*[, alpha, l1_ratio, ...])

Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer For more details on this class, see sklearn.linear_model.MultiTaskElasticNet

MultiTaskElasticNetCV(*[, l1_ratio, eps, ...])

Multi-task L1/L2 ElasticNet with built-in cross-validation For more details on this class, see sklearn.linear_model.MultiTaskElasticNetCV

MultiTaskLasso(*[, alpha, fit_intercept, ...])

Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer For more details on this class, see sklearn.linear_model.MultiTaskLasso

MultiTaskLassoCV(*[, eps, n_alphas, alphas, ...])

Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer For more details on this class, see sklearn.linear_model.MultiTaskLassoCV

OrthogonalMatchingPursuit(*[, ...])

Orthogonal Matching Pursuit model (OMP) For more details on this class, see sklearn.linear_model.OrthogonalMatchingPursuit

PassiveAggressiveClassifier(*[, C, ...])

Passive Aggressive Classifier For more details on this class, see sklearn.linear_model.PassiveAggressiveClassifier

PassiveAggressiveRegressor(*[, C, ...])

Passive Aggressive Regressor For more details on this class, see sklearn.linear_model.PassiveAggressiveRegressor

Perceptron(*[, penalty, alpha, l1_ratio, ...])

Linear perceptron classifier For more details on this class, see sklearn.linear_model.Perceptron

PoissonRegressor(*[, alpha, fit_intercept, ...])

Generalized Linear Model with a Poisson distribution For more details on this class, see sklearn.linear_model.PoissonRegressor

RANSACRegressor(*[, estimator, min_samples, ...])

RANSAC (RANdom SAmple Consensus) algorithm For more details on this class, see sklearn.linear_model.RANSACRegressor

Ridge(*[, alpha, fit_intercept, copy_X, ...])

Linear least squares with l2 regularization For more details on this class, see sklearn.linear_model.Ridge

RidgeClassifier(*[, alpha, fit_intercept, ...])

Classifier using Ridge regression For more details on this class, see sklearn.linear_model.RidgeClassifier

RidgeClassifierCV(*[, alphas, ...])

Ridge classifier with built-in cross-validation For more details on this class, see sklearn.linear_model.RidgeClassifierCV

RidgeCV(*[, alphas, fit_intercept, scoring, ...])

Ridge regression with built-in cross-validation For more details on this class, see sklearn.linear_model.RidgeCV

SGDClassifier(*[, loss, penalty, alpha, ...])

Linear classifiers (SVM, logistic regression, etc For more details on this class, see sklearn.linear_model.SGDClassifier

SGDOneClassSVM(*[, nu, fit_intercept, ...])

Solves linear One-Class SVM using Stochastic Gradient Descent For more details on this class, see sklearn.linear_model.SGDOneClassSVM

SGDRegressor(*[, loss, penalty, alpha, ...])

Linear model fitted by minimizing a regularized empirical loss with SGD For more details on this class, see sklearn.linear_model.SGDRegressor

TheilSenRegressor(*[, fit_intercept, ...])

Theil-Sen Estimator: robust multivariate regression model For more details on this class, see sklearn.linear_model.TheilSenRegressor

TweedieRegressor(*[, power, alpha, ...])

Generalized Linear Model with a Tweedie distribution For more details on this class, see sklearn.linear_model.TweedieRegressor

snowflake.ml.modeling.manifold

Classes

Isomap(*[, n_neighbors, radius, ...])

Isomap Embedding For more details on this class, see sklearn.manifold.Isomap

MDS(*[, n_components, metric, n_init, ...])

Multidimensional scaling For more details on this class, see sklearn.manifold.MDS

SpectralEmbedding(*[, n_components, ...])

Spectral embedding for non-linear dimensionality reduction For more details on this class, see sklearn.manifold.SpectralEmbedding

TSNE(*[, n_components, perplexity, ...])

T-distributed Stochastic Neighbor Embedding For more details on this class, see sklearn.manifold.TSNE

snowflake.ml.modeling.metrics

Functions

accuracy_score(*, df, y_true_col_names, ...)

Accuracy classification score.

confusion_matrix(*, df, y_true_col_name, ...)

Compute confusion matrix to evaluate the accuracy of a classification.

correlation(*, df[, columns])

Pearson correlation matrix for the columns in a snowpark dataframe.

covariance(*, df[, columns, ddof])

Covariance matrix for the columns in a snowpark dataframe.

d2_absolute_error_score(*, df, ...[, ...])

D^2 regression score function, fraction of absolute error explained.

d2_pinball_score(*, df, y_true_col_names, ...)

D^2 regression score function, fraction of pinball loss explained.

explained_variance_score(*, df, ...[, ...])

Explained variance regression score function.

f1_score(*, df, y_true_col_names, ...[, ...])

Compute the F1 score, also known as balanced F-score or F-measure.

fbeta_score(*, df, y_true_col_names, ...[, ...])

Compute the F-beta score.

log_loss(*, df, y_true_col_names, ...[, ...])

Log loss, aka logistic loss or cross-entropy loss.

mean_absolute_error(*, df, y_true_col_names, ...)

Mean absolute error regression loss.

mean_absolute_percentage_error(*, df, ...[, ...])

Mean absolute percentage error (MAPE) regression loss.

mean_squared_error(*, df, y_true_col_names, ...)

Mean squared error regression loss.

precision_recall_curve(*, df, ...[, ...])

Compute precision-recall pairs for different probability thresholds.

precision_recall_fscore_support(*, df, ...)

Compute precision, recall, F-measure and support for each class.

precision_score(*, df, y_true_col_names, ...)

Compute the precision.

r2_score(*, df, y_true_col_name, y_pred_col_name)

R^2 (coefficient of determination) regression score function.

recall_score(*, df, y_true_col_names, ...[, ...])

Compute the recall.

roc_auc_score(*, df, y_true_col_names, ...)

Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.

roc_curve(*, df, y_true_col_name, ...[, ...])

Compute Receiver operating characteristic (ROC).

snowflake.ml.modeling.mixture

Classes

BayesianGaussianMixture(*[, n_components, ...])

Variational Bayesian estimation of a Gaussian mixture For more details on this class, see sklearn.mixture.BayesianGaussianMixture

GaussianMixture(*[, n_components, ...])

Gaussian Mixture For more details on this class, see sklearn.mixture.GaussianMixture

snowflake.ml.modeling.model_selection

Classes

GridSearchCV(*, estimator, param_grid[, ...])

Exhaustive search over specified parameter values for an estimator For more details on this class, see sklearn.model_selection.GridSearchCV

RandomizedSearchCV(*, estimator, ...[, ...])

Randomized search on hyper parameters For more details on this class, see sklearn.model_selection.RandomizedSearchCV

snowflake.ml.modeling.multiclass

Classes

OneVsOneClassifier(*, estimator[, n_jobs, ...])

One-vs-one multiclass strategy For more details on this class, see sklearn.multiclass.OneVsOneClassifier

OneVsRestClassifier(*, estimator[, n_jobs, ...])

One-vs-the-rest (OvR) multiclass strategy For more details on this class, see sklearn.multiclass.OneVsRestClassifier

OutputCodeClassifier(*, estimator[, ...])

(Error-Correcting) Output-Code multiclass strategy For more details on this class, see sklearn.multiclass.OutputCodeClassifier

snowflake.ml.modeling.naive_bayes

Classes

BernoulliNB(*[, alpha, force_alpha, ...])

Naive Bayes classifier for multivariate Bernoulli models For more details on this class, see sklearn.naive_bayes.BernoulliNB

CategoricalNB(*[, alpha, force_alpha, ...])

Naive Bayes classifier for categorical features For more details on this class, see sklearn.naive_bayes.CategoricalNB

ComplementNB(*[, alpha, force_alpha, ...])

The Complement Naive Bayes classifier described in Rennie et al For more details on this class, see sklearn.naive_bayes.ComplementNB

GaussianNB(*[, priors, var_smoothing, ...])

Gaussian Naive Bayes (GaussianNB) For more details on this class, see sklearn.naive_bayes.GaussianNB

MultinomialNB(*[, alpha, force_alpha, ...])

Naive Bayes classifier for multinomial models For more details on this class, see sklearn.naive_bayes.MultinomialNB

snowflake.ml.modeling.neighbors

Classes

KernelDensity(*[, bandwidth, algorithm, ...])

Kernel Density Estimation For more details on this class, see sklearn.neighbors.KernelDensity

KNeighborsClassifier(*[, n_neighbors, ...])

Classifier implementing the k-nearest neighbors vote For more details on this class, see sklearn.neighbors.KNeighborsClassifier

KNeighborsRegressor(*[, n_neighbors, ...])

Regression based on k-nearest neighbors For more details on this class, see sklearn.neighbors.KNeighborsRegressor

LocalOutlierFactor(*[, n_neighbors, ...])

Unsupervised Outlier Detection using the Local Outlier Factor (LOF) For more details on this class, see sklearn.neighbors.LocalOutlierFactor

NearestCentroid(*[, metric, ...])

Nearest centroid classifier For more details on this class, see sklearn.neighbors.NearestCentroid

NearestNeighbors(*[, n_neighbors, radius, ...])

Unsupervised learner for implementing neighbor searches For more details on this class, see sklearn.neighbors.NearestNeighbors

NeighborhoodComponentsAnalysis(*[, ...])

Neighborhood Components Analysis For more details on this class, see sklearn.neighbors.NeighborhoodComponentsAnalysis

RadiusNeighborsClassifier(*[, radius, ...])

Classifier implementing a vote among neighbors within a given radius For more details on this class, see sklearn.neighbors.RadiusNeighborsClassifier

RadiusNeighborsRegressor(*[, radius, ...])

Regression based on neighbors within a fixed radius For more details on this class, see sklearn.neighbors.RadiusNeighborsRegressor

snowflake.ml.modeling.neural_network

Classes

BernoulliRBM(*[, n_components, ...])

Bernoulli Restricted Boltzmann Machine (RBM) For more details on this class, see sklearn.neural_network.BernoulliRBM

MLPClassifier(*[, hidden_layer_sizes, ...])

Multi-layer Perceptron classifier For more details on this class, see sklearn.neural_network.MLPClassifier

MLPRegressor(*[, hidden_layer_sizes, ...])

Multi-layer Perceptron regressor For more details on this class, see sklearn.neural_network.MLPRegressor

snowflake.ml.modeling.pipeline

Classes

Pipeline(steps)

Pipeline of transforms.

snowflake.ml.modeling.preprocessing

StandardScaler(*[, with_mean, with_std, ...])

Standardizes features by removing the mean and scaling to unit variance.

OrdinalEncoder(*[, categories, ...])

Encodes categorical features as an integer array.

MinMaxScaler(*[, feature_range, clip, ...])

Transforms features by scaling each feature to a given range, by default between zero and one.

LabelEncoder([input_cols, output_cols, ...])

Encodes target labels with values between 0 and n_classes-1.

RobustScaler(*[, with_centering, ...])

Scales features using statistics that are robust to outliers.

KBinsDiscretizer(*[, n_bins, encode, ...])

Bin continuous data into intervals.

MaxAbsScaler(*[, input_cols, output_cols, ...])

Scale each feature by its maximum absolute value.

Normalizer(*[, norm, input_cols, ...])

Normalize samples individually to each row's unit norm.

OneHotEncoder(*[, categories, drop, sparse, ...])

Encode categorical features as a one-hot numeric array.

Binarizer(*[, threshold, input_cols, ...])

Binarizes data (sets feature values to 0 or 1) according to the given threshold.

PolynomialFeatures(*[, degree, ...])

Generate polynomial and interaction features For more details on this class, see sklearn.preprocessing.PolynomialFeatures

snowflake.ml.modeling.semi_supervised

Classes

LabelPropagation(*[, kernel, gamma, ...])

Label Propagation classifier For more details on this class, see sklearn.semi_supervised.LabelPropagation

LabelSpreading(*[, kernel, gamma, ...])

LabelSpreading model for semi-supervised learning For more details on this class, see sklearn.semi_supervised.LabelSpreading

snowflake.ml.modeling.svm

Classes

LinearSVC(*[, penalty, loss, dual, tol, C, ...])

Linear Support Vector Classification For more details on this class, see sklearn.svm.LinearSVC

LinearSVR(*[, epsilon, tol, C, loss, ...])

Linear Support Vector Regression For more details on this class, see sklearn.svm.LinearSVR

NuSVC(*[, nu, kernel, degree, gamma, coef0, ...])

Nu-Support Vector Classification For more details on this class, see sklearn.svm.NuSVC

NuSVR(*[, nu, C, kernel, degree, gamma, ...])

Nu Support Vector Regression For more details on this class, see sklearn.svm.NuSVR

SVC(*[, C, kernel, degree, gamma, coef0, ...])

C-Support Vector Classification For more details on this class, see sklearn.svm.SVC

SVR(*[, kernel, degree, gamma, coef0, tol, ...])

Epsilon-Support Vector Regression For more details on this class, see sklearn.svm.SVR

snowflake.ml.modeling.tree

Classes

DecisionTreeClassifier(*[, criterion, ...])

A decision tree classifier For more details on this class, see sklearn.tree.DecisionTreeClassifier

DecisionTreeRegressor(*[, criterion, ...])

A decision tree regressor For more details on this class, see sklearn.tree.DecisionTreeRegressor

ExtraTreeClassifier(*[, criterion, ...])

An extremely randomized tree classifier For more details on this class, see sklearn.tree.ExtraTreeClassifier

ExtraTreeRegressor(*[, criterion, splitter, ...])

An extremely randomized tree regressor For more details on this class, see sklearn.tree.ExtraTreeRegressor

snowflake.ml.modeling.xgboost

Classes

XGBClassifier(*[, objective, ...])

Implementation of the scikit-learn API for XGBoost classification For more details on this class, see xgboost.XGBClassifier

XGBRegressor(*[, objective, input_cols, ...])

Implementation of the scikit-learn API for XGBoost regression For more details on this class, see xgboost.XGBRegressor

XGBRFClassifier(*[, learning_rate, ...])

scikit-learn API for XGBoost random forest classification For more details on this class, see xgboost.XGBRFClassifier

XGBRFRegressor(*[, learning_rate, ...])

scikit-learn API for XGBoost random forest regression For more details on this class, see xgboost.XGBRFRegressor
Language: English