# SOME DESCRIPTIVE TITLE.
# Copyright (C) 1999-2020, The Alibaba Group Holding Ltd.
# This file is distributed under the same license as the mars package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2020.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: mars 0.5.0a2\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2021-09-02 18:08+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: LANGUAGE <LL@li.org>\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Generated-By: Babel 2.7.0\n"

#: ../../source/reference/learn/reference.rst:5
msgid "Mars Learn"
msgstr ""

#: ../../source/reference/learn/reference.rst:7
msgid "This is the class and function reference of Mars learn."
msgstr "这是 Mars learn 的类和函参考。"

#: ../../source/reference/learn/reference.rst:17
msgid "Clustering"
msgstr ""

#: ../../source/reference/learn/reference.rst:24
msgid "Classes"
msgstr ""

#: ../../source/reference/learn/reference.rst:31:<autosummary>:1
msgid ""
":obj:`cluster.KMeans <mars.learn.cluster.KMeans>`\\ \\(\\[n\\_clusters\\,"
" init\\, n\\_init\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:31:<autosummary>:1
msgid "K-Means clustering."
msgstr ""

#: ../../source/reference/learn/reference.rst:33
msgid "Functions"
msgstr ""

#: ../../source/reference/learn/reference.rst:40:<autosummary>:1
msgid ""
":obj:`cluster.k_means <mars.learn.cluster.k_means>`\\ \\(X\\, "
"n\\_clusters\\[\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:40:<autosummary>:1
msgid "K-means clustering algorithm."
msgstr ""

#: ../../source/reference/learn/reference.rst:44
msgid "Datasets"
msgstr "数据集"

#: ../../source/reference/learn/reference.rst:51
msgid "Samples generator"
msgstr "样本生成器"

#: ../../source/reference/learn/reference.rst:61:<autosummary>:1
msgid ""
":obj:`datasets.make_blobs <mars.learn.datasets.make_blobs>`\\ "
"\\(\\[n\\_samples\\, n\\_features\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:61:<autosummary>:1
msgid "Generate isotropic Gaussian blobs for clustering."
msgstr ""

#: ../../source/reference/learn/reference.rst:61:<autosummary>:1
msgid ""
":obj:`datasets.make_classification "
"<mars.learn.datasets.make_classification>`\\ \\(\\[n\\_samples\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:61:<autosummary>:1
msgid "Generate a random n-class classification problem."
msgstr ""

#: ../../source/reference/learn/reference.rst:61:<autosummary>:1
msgid ""
":obj:`datasets.make_low_rank_matrix "
"<mars.learn.datasets.make_low_rank_matrix>`\\ \\(\\[n\\_samples\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:61:<autosummary>:1
msgid "Generate a mostly low rank matrix with bell-shaped singular values"
msgstr ""

#: ../../source/reference/learn/reference.rst:65
msgid "Matrix Decomposition"
msgstr "矩阵分解"

#: ../../source/reference/learn/reference.rst:78:<autosummary>:1
msgid ""
":obj:`decomposition.PCA <mars.learn.decomposition.PCA>`\\ "
"\\(\\[n\\_components\\, copy\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:78:<autosummary>:1
msgid "Principal component analysis (PCA)"
msgstr ""

#: ../../source/reference/learn/reference.rst:78:<autosummary>:1
msgid ""
":obj:`decomposition.TruncatedSVD "
"<mars.learn.decomposition.TruncatedSVD>`\\ \\(\\[n\\_components\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:78:<autosummary>:1
msgid "Dimensionality reduction using truncated SVD (aka LSA)."
msgstr ""

#: ../../source/reference/learn/reference.rst:82
msgid "Ensemble Methods"
msgstr "集成方法"

#: ../../source/reference/learn/reference.rst:95:<autosummary>:1
msgid ""
":obj:`ensemble.BlockwiseVotingClassifier "
"<mars.learn.ensemble.BlockwiseVotingClassifier>`\\ \\(estimator\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:95:<autosummary>:1
msgid "Blockwise training and ensemble voting classifier."
msgstr ""

#: ../../source/reference/learn/reference.rst:95:<autosummary>:1
msgid ""
":obj:`ensemble.BlockwiseVotingRegressor "
"<mars.learn.ensemble.BlockwiseVotingRegressor>`\\ \\(estimator\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:95:<autosummary>:1
msgid "Blockwise training and ensemble voting regressor."
msgstr ""

#: ../../source/reference/learn/reference.rst:99
msgid "Linear Models"
msgstr "线性模型"

#: ../../source/reference/learn/reference.rst:102
msgid "Classical linear regressors"
msgstr ""

#: ../../source/reference/learn/reference.rst:110:<autosummary>:1
msgid ""
":obj:`linear_model.LinearRegression "
"<mars.learn.linear_model.LinearRegression>`\\ \\(\\*\\[\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:110:<autosummary>:1
msgid "Ordinary least squares Linear Regression."
msgstr ""

#: ../../source/reference/learn/reference.rst:114
msgid "Metrics"
msgstr "评估"

#: ../../source/reference/learn/reference.rst:123
msgid "Classification metrics"
msgstr "分类评估"

#: ../../source/reference/learn/reference.rst:131:<autosummary>:1
msgid ""
":obj:`metrics.accuracy_score <mars.learn.metrics.accuracy_score>`\\ "
"\\(y\\_true\\, y\\_pred\\[\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:131:<autosummary>:1
msgid "Accuracy classification score."
msgstr ""

#: ../../source/reference/learn/reference.rst:131:<autosummary>:1
msgid ""
":obj:`metrics.auc <mars.learn.metrics.auc>`\\ \\(x\\, y\\[\\, session\\, "
"run\\_kwargs\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:131:<autosummary>:1
msgid "Compute Area Under the Curve (AUC) using the trapezoidal rule"
msgstr ""

#: ../../source/reference/learn/reference.rst:131:<autosummary>:1
msgid ""
":obj:`metrics.roc_curve <mars.learn.metrics.roc_curve>`\\ \\(y\\_true\\, "
"y\\_score\\[\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:131:<autosummary>:1
msgid "Compute Receiver operating characteristic (ROC)"
msgstr ""

#: ../../source/reference/learn/reference.rst:133
msgid "Regression metrics"
msgstr "分类评估"

#: ../../source/reference/learn/reference.rst:140:<autosummary>:1
msgid ""
":obj:`metrics.r2_score <mars.learn.metrics.r2_score>`\\ \\(y\\_true\\, "
"y\\_pred\\, \\*\\[\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:140:<autosummary>:1
msgid ":math:`R^2` (coefficient of determination) regression score function."
msgstr ""

#: ../../source/reference/learn/reference.rst:142
msgid "Pairwise metrics"
msgstr "Pairwise 评估"

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid ""
":obj:`metrics.pairwise.cosine_similarity "
"<mars.learn.metrics.pairwise.cosine_similarity>`\\ \\(X\\[\\, Y\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid "Compute cosine similarity between samples in X and Y."
msgstr ""

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid ""
":obj:`metrics.pairwise.cosine_distances "
"<mars.learn.metrics.pairwise.cosine_distances>`\\ \\(X\\[\\, Y\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid "Compute cosine distance between samples in X and Y."
msgstr ""

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid ""
":obj:`metrics.pairwise.euclidean_distances "
"<mars.learn.metrics.pairwise.euclidean_distances>`\\ \\(X\\[\\, Y\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid ""
"Considering the rows of X (and Y=X) as vectors, compute the distance "
"matrix between each pair of vectors."
msgstr ""

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid ""
":obj:`metrics.pairwise.haversine_distances "
"<mars.learn.metrics.pairwise.haversine_distances>`\\ \\(X\\[\\, Y\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid "Compute the Haversine distance between samples in X and Y"
msgstr ""

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid ""
":obj:`metrics.pairwise.manhattan_distances "
"<mars.learn.metrics.pairwise.manhattan_distances>`\\ \\(X\\[\\, Y\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid "Compute the L1 distances between the vectors in X and Y."
msgstr ""

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid ""
":obj:`metrics.pairwise.rbf_kernel "
"<mars.learn.metrics.pairwise.rbf_kernel>`\\ \\(X\\[\\, Y\\, gamma\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid "Compute the rbf (gaussian) kernel between X and Y."
msgstr ""

#: ../../source/reference/learn/reference.rst:160:<autosummary>:1
msgid ""
":obj:`metrics.pairwise_distances "
"<mars.learn.metrics.pairwise_distances>`\\ \\(X\\[\\, Y\\, metric\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:164
msgid "Model Selection"
msgstr "模型选择"

#: ../../source/reference/learn/reference.rst:167
msgid "Splitter Classes"
msgstr "划分类"

#: ../../source/reference/learn/reference.rst:175:<autosummary>:1
msgid ""
":obj:`model_selection.KFold <mars.learn.model_selection.KFold>`\\ "
"\\(\\[n\\_splits\\, shuffle\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:175:<autosummary>:1
msgid "K-Folds cross-validator"
msgstr ""

#: ../../source/reference/learn/reference.rst:177
msgid "Splitter Functions"
msgstr "划分函数"

#: ../../source/reference/learn/reference.rst:185:<autosummary>:1
msgid ""
":obj:`model_selection.train_test_split "
"<mars.learn.model_selection.train_test_split>`\\ \\(\\*arrays\\, ...\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:185:<autosummary>:1
msgid "Split arrays or matrices into random train and test subsets"
msgstr ""

#: ../../source/reference/learn/reference.rst:189
msgid "Nearest Neighbors"
msgstr "最邻近"

#: ../../source/reference/learn/reference.rst:201:<autosummary>:1
msgid ""
":obj:`neighbors.NearestNeighbors "
"<mars.learn.neighbors.NearestNeighbors>`\\ \\(\\[n\\_neighbors\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:205
msgid "Preprocessing and Normalization"
msgstr "预处理和标准化"

#: ../../source/reference/learn/reference.rst:221:<autosummary>:1
msgid ""
":obj:`preprocessing.LabelBinarizer "
"<mars.learn.preprocessing.LabelBinarizer>`\\ \\(\\*\\[\\, neg\\_label\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:221:<autosummary>:1
msgid "Binarize labels in a one-vs-all fashion."
msgstr ""

#: ../../source/reference/learn/reference.rst:221:<autosummary>:1
msgid ""
":obj:`preprocessing.MinMaxScaler "
"<mars.learn.preprocessing.MinMaxScaler>`\\ \\(\\[feature\\_range\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:221:<autosummary>:1
msgid "Transform features by scaling each feature to a given range."
msgstr ""

#: ../../source/reference/learn/reference.rst:221:<autosummary>:1
msgid ""
":obj:`preprocessing.minmax_scale "
"<mars.learn.preprocessing.minmax_scale>`\\ \\(X\\[\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:221:<autosummary>:1
msgid ""
":obj:`preprocessing.label_binarize "
"<mars.learn.preprocessing.label_binarize>`\\ \\(y\\, \\*\\, classes\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:221:<autosummary>:1
msgid ""
":obj:`preprocessing.normalize <mars.learn.preprocessing.normalize>`\\ "
"\\(X\\[\\, norm\\, axis\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:221:<autosummary>:1
msgid "Scale input vectors individually to unit norm (vector length)."
msgstr ""

#: ../../source/reference/learn/reference.rst:225
msgid "Semi-Supervised Learning"
msgstr "半监督学习"

#: ../../source/reference/learn/reference.rst:237:<autosummary>:1
msgid ""
":obj:`semi_supervised.LabelPropagation "
"<mars.learn.semi_supervised.LabelPropagation>`\\ \\(\\[kernel\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:237:<autosummary>:1
msgid "Label Propagation classifier"
msgstr ""

#: ../../source/reference/learn/reference.rst:241
msgid "Utilities"
msgstr "工具"

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid ""
":obj:`utils.assert_all_finite <mars.learn.utils.assert_all_finite>`\\ "
"\\(X\\[\\, allow\\_nan\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid ""
":obj:`utils.check_X_y <mars.learn.utils.check_X_y>`\\ \\(X\\, y\\[\\, "
"accept\\_sparse\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid "Input validation for standard estimators."
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid ""
":obj:`utils.check_array <mars.learn.utils.check_array>`\\ \\(array\\[\\, "
"accept\\_sparse\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid "Input validation on a tensor, list, sparse matrix or similar."
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid ""
":obj:`utils.check_consistent_length "
"<mars.learn.utils.check_consistent_length>`\\ \\(\\*arrays\\[\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid "Check that all arrays have consistent first dimensions."
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid ""
":obj:`utils.multiclass.type_of_target "
"<mars.learn.utils.multiclass.type_of_target>`\\ \\(y\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid "Determine the type of data indicated by the target."
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid ""
":obj:`utils.multiclass.is_multilabel "
"<mars.learn.utils.multiclass.is_multilabel>`\\ \\(y\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid "Check if ``y`` is in a multilabel format."
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid ""
":obj:`utils.shuffle <mars.learn.utils.shuffle>`\\ \\(\\*arrays\\, "
"\\*\\*options\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid ""
":obj:`utils.validation.check_is_fitted "
"<mars.learn.utils.validation.check_is_fitted>`\\ \\(estimator\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid "Perform is_fitted validation for estimator."
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid ""
":obj:`utils.validation.column_or_1d "
"<mars.learn.utils.validation.column_or_1d>`\\ \\(y\\[\\, warn\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:261:<autosummary>:1
msgid "Ravel column or 1d numpy array, else raises an error"
msgstr ""

#: ../../source/reference/learn/reference.rst:265
msgid "LightGBM Integration"
msgstr "LightGBM 集成"

#: ../../source/reference/learn/reference.rst:279:<autosummary>:1
msgid ""
":obj:`contrib.lightgbm.LGBMClassifier "
"<mars.learn.contrib.lightgbm.LGBMClassifier>`\\ \\(\\*args\\, "
"\\*\\*kwargs\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:279:<autosummary>:1
msgid ""
":obj:`contrib.lightgbm.LGBMRegressor "
"<mars.learn.contrib.lightgbm.LGBMRegressor>`\\ \\(\\*args\\, "
"\\*\\*kwargs\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:279:<autosummary>:1
msgid ""
":obj:`contrib.lightgbm.LGBMRanker "
"<mars.learn.contrib.lightgbm.LGBMRanker>`\\ \\(\\*args\\, \\*\\*kwargs\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:283
msgid "PyTorch Integration"
msgstr "PyTorch 集成"

#: ../../source/reference/learn/reference.rst:300:<autosummary>:1
msgid ""
":obj:`contrib.pytorch.run_pytorch_script "
"<mars.learn.contrib.pytorch.run_pytorch_script>`\\ \\(script\\, ...\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:300:<autosummary>:1
msgid "Run PyTorch script in Mars cluster."
msgstr ""

#: ../../source/reference/learn/reference.rst:300:<autosummary>:1
msgid ""
":obj:`contrib.pytorch.MarsDataset "
"<mars.learn.contrib.pytorch.MarsDataset>`\\ \\(\\*tileables\\[\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:300:<autosummary>:1
msgid "MarsDataset that inherit from torch.utils.data.Dataset."
msgstr ""

#: ../../source/reference/learn/reference.rst:300:<autosummary>:1
msgid ""
":obj:`contrib.pytorch.SequentialSampler "
"<mars.learn.contrib.pytorch.SequentialSampler>`\\ \\(data\\_source\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:300:<autosummary>:1
msgid "\"Samples elements sequentially, always in the same order."
msgstr ""

#: ../../source/reference/learn/reference.rst:300:<autosummary>:1
msgid ""
":obj:`contrib.pytorch.RandomSampler "
"<mars.learn.contrib.pytorch.RandomSampler>`\\ \\(data\\_source\\[\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:300:<autosummary>:1
msgid "\""
msgstr ""

#: ../../source/reference/learn/reference.rst:300:<autosummary>:1
msgid ""
":obj:`contrib.pytorch.SubsetRandomSampler "
"<mars.learn.contrib.pytorch.SubsetRandomSampler>`\\ \\(indices\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:300:<autosummary>:1
msgid ""
"Samples elements randomly from a given list of indices, without "
"replacement."
msgstr ""

#: ../../source/reference/learn/reference.rst:300:<autosummary>:1
msgid ""
":obj:`contrib.pytorch.DistributedSampler "
"<mars.learn.contrib.pytorch.DistributedSampler>`\\ \\(dataset\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:300:<autosummary>:1
msgid "Sampler that restricts data loading to a subset of the dataset."
msgstr ""

#: ../../source/reference/learn/reference.rst:304
msgid "StatsModels Integration"
msgstr "StatsModels 集成"

#: ../../source/reference/learn/reference.rst:317:<autosummary>:1
msgid ""
":obj:`contrib.statsmodels.MarsDistributedModel "
"<mars.learn.contrib.statsmodels.MarsDistributedModel>`\\ \\(\\[...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:317:<autosummary>:1
msgid ""
":obj:`contrib.statsmodels.MarsResults "
"<mars.learn.contrib.statsmodels.MarsResults>`\\ \\(model\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:321
msgid "TensorFlow Integration"
msgstr "TensorFlow 集成"

#: ../../source/reference/learn/reference.rst:334:<autosummary>:1
msgid ""
":obj:`contrib.tensorflow.run_tensorflow_script "
"<mars.learn.contrib.tensorflow.run_tensorflow_script>`\\ \\(...\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:334:<autosummary>:1
msgid "Run TensorFlow script in Mars cluster."
msgstr ""

#: ../../source/reference/learn/reference.rst:334:<autosummary>:1
msgid ""
":obj:`contrib.tensorflow.gen_tensorflow_dataset "
"<mars.learn.contrib.tensorflow.gen_tensorflow_dataset>`\\ \\(tensors\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:334:<autosummary>:1
msgid "convert mars data type to tf.data.Dataset."
msgstr ""

#: ../../source/reference/learn/reference.rst:338
msgid "XGBoost Integration"
msgstr "XGBoost 集成"

#: ../../source/reference/learn/reference.rst:353:<autosummary>:1
msgid ""
":obj:`contrib.xgboost.MarsDMatrix "
"<mars.learn.contrib.xgboost.MarsDMatrix>`\\ \\(data\\[\\, label\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:353:<autosummary>:1
msgid ""
":obj:`contrib.xgboost.train <mars.learn.contrib.xgboost.train>`\\ "
"\\(params\\, dtrain\\[\\, evals\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:353:<autosummary>:1
msgid "Train XGBoost model in Mars manner."
msgstr ""

#: ../../source/reference/learn/reference.rst:353:<autosummary>:1
msgid ""
":obj:`contrib.xgboost.predict <mars.learn.contrib.xgboost.predict>`\\ "
"\\(model\\, data\\[\\, ...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:353:<autosummary>:1
msgid ""
":obj:`contrib.xgboost.XGBClassifier "
"<mars.learn.contrib.xgboost.XGBClassifier>`\\ \\(\\[max\\_depth\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:353:<autosummary>:1
msgid "Implementation of the scikit-learn API for XGBoost classification."
msgstr ""

#: ../../source/reference/learn/reference.rst:353:<autosummary>:1
msgid ""
":obj:`contrib.xgboost.XGBRegressor "
"<mars.learn.contrib.xgboost.XGBRegressor>`\\ \\(\\[max\\_depth\\, "
"...\\]\\)"
msgstr ""

#: ../../source/reference/learn/reference.rst:353:<autosummary>:1
msgid "Implementation of the scikit-learn API for XGBoost regressor."
msgstr ""

