# 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: 2020-06-16 16:57+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"
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"Generated-By: Babel 2.7.0\n"

#: ../../source/reference/learn/generated/mars.learn.metrics.accuracy_score.rst:2
msgid "mars.learn.metrics.accuracy\\_score"
msgstr ""

#: mars.learn.metrics.accuracy_score:1 of
msgid "Accuracy classification score."
msgstr ""

#: mars.learn.metrics.accuracy_score:3 of
msgid ""
"In multilabel classification, this function computes subset accuracy: the"
" set of labels predicted for a sample must *exactly* match the "
"corresponding set of labels in y_true."
msgstr ""

#: mars.learn.metrics.accuracy_score:7 of
msgid "Read more in the :ref:`User Guide <accuracy_score>`."
msgstr ""

#: mars.learn.metrics.accuracy_score of
msgid "Parameters"
msgstr ""

#: mars.learn.metrics.accuracy_score:9 of
msgid "Ground truth (correct) labels."
msgstr ""

#: mars.learn.metrics.accuracy_score:11 of
msgid "Predicted labels, as returned by a classifier."
msgstr ""

#: mars.learn.metrics.accuracy_score:13 of
msgid ""
"If ``False``, return the number of correctly classified samples. "
"Otherwise, return the fraction of correctly classified samples."
msgstr ""

#: mars.learn.metrics.accuracy_score:16 of
msgid "Sample weights."
msgstr ""

#: mars.learn.metrics.accuracy_score of
msgid "Returns"
msgstr ""

#: mars.learn.metrics.accuracy_score:19 of
msgid ""
"**score** -- If ``normalize == True``, return the fraction of correctly "
"classified samples (float), else returns the number of correctly "
"classified samples (int).  The best performance is 1 with ``normalize == "
"True`` and the number of samples with ``normalize == False``."
msgstr ""

#: mars.learn.metrics.accuracy_score:19 of
msgid ""
"**score** -- If ``normalize == True``, return the fraction of correctly "
"classified samples (float), else returns the number of correctly "
"classified samples (int)."
msgstr ""

#: mars.learn.metrics.accuracy_score:23 of
msgid ""
"The best performance is 1 with ``normalize == True`` and the number of "
"samples with ``normalize == False``."
msgstr ""

#: mars.learn.metrics.accuracy_score of
msgid "Return type"
msgstr ""

#: mars.learn.metrics.accuracy_score:27 of
msgid ":func:`jaccard_score`, :func:`hamming_loss`, :func:`zero_one_loss`"
msgstr ""

#: mars.learn.metrics.accuracy_score:30 of
msgid "Notes"
msgstr ""

#: mars.learn.metrics.accuracy_score:31 of
msgid ""
"In binary and multiclass classification, this function is equal to the "
"``jaccard_score`` function."
msgstr ""

#: mars.learn.metrics.accuracy_score:35 of
msgid "Examples"
msgstr ""

#: mars.learn.metrics.accuracy_score:44 of
msgid "In the multilabel case with binary label indicators:"
msgstr ""

