blocks|key|741362|text|scikit中的大多数分类器都有一个内置的score()函数，您可以在其中输入X_test和y_test，它将为该估计器输出适当的度量。对于分类估计器，主要是'mean+accuracy'。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|741363|此外，sklearn.metrics还有许多可用的功能，可以输出不同的度量标准，如accuracy、precision、recall等。|741364|对于你的具体问题，你需要accuracy_score|741365|from+sklearn.metrics+import+accuracy_score
score+=+accuracy_score(iris.target,+pr)|code-block|syntax|javascript|741366|entityMap|0|LINK|mutability|MUTABLE|url|http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB.score|1|http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics|2|http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score^0|L|7|27|F|L|7|0|0|3|F|15|8|1E|9|1O|6|3|F|1|0|C|E|C|E|2|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@$9|11|A|12|B|C]|$9|13|A|14|B|C]]|D|@$9|15|A|16|1|17]]|E|$]]|$1|F|3|G|5|6|7|18|8|@$9|19|A|1A|B|C]|$9|1B|A|1C|B|C]|$9|1D|A|1E|B|C]|$9|1F|A|1G|B|C]]|D|@$9|1H|A|1I|1|1J]]|E|$]]|$1|H|3|I|5|6|7|1K|8|@$9|1L|A|1M|B|C]]|D|@$9|1N|A|1O|1|1P]]|E|$]]|$1|J|3|K|5|L|7|1Q|8|@]|D|@]|E|$M|N]]|$1|O|3|-4|5|6|7|1R|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]|W|$5|R|S|T|E|$U|X]]|Y|$5|R|S|T|E|$U|Z]]]]

Most classifiers in scikit have an inbuilt <a href="http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB.score" rel="noreferrer"><code>score()</code></a> function, in which you can input your X_test and y_test and it will output the appropriate metric for that estimator. For classification estimators it is mostly <code>'mean accuracy'</code>.

Also <a href="http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics" rel="noreferrer"><code>sklearn.metrics</code></a> have many functions available which will output different metrics like <code>accuracy</code>, <code>precision</code>, <code>recall</code> etc.

For your specific question you need <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score" rel="noreferrer"><code>accuracy_score</code></a>

<pre><code>from sklearn.metrics import accuracy_score
score = accuracy_score(iris.target, pr)
</code></pre>

blocks|key|1957862|text|您可以使用accuracy_score，查找文档这里。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1957863|像这样实施-|1957864|from+sklearn.metrics+import+accuracy_score
accuracy+=+accuracy_score(prediction,+labels_test)|code-block|syntax|javascript|1957865|这将返回一个浮点值。浮点数描述(正确分类的点数)/(测试集中的总点数)|1957866|entityMap|0|LINK|mutability|MUTABLE|url|http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html^0|5|E|O|2|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@$9|Z|A|10|1|11]]|E|$]]|$1|F|3|G|5|6|7|12|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|13|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|14|8|@]|D|@]|E|$]]|$1|O|3|-4|5|6|7|15|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]]]

You can use <code>accuracy_score</code>, find documentation <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html" rel="noreferrer" title="this">here</a>.

Implement like this - 

<pre><code>from sklearn.metrics import accuracy_score
accuracy = accuracy_score(prediction, labels_test)
</code></pre>

This will return a float value. The float value describes (number of points classified correctly) / (total number of points in your test set)

blocks|key|239430|text|您必须从accuracy_score中导入sklearn.metrics。应该像下面这样，|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|239431|from+sklearn.metrics+import+accuracy_score
print+accuracy_score(predictions,test+set+of+labels)|code-block|syntax|javascript|239432|准确的公式是：|239433|正确分类的点数/测试集中的所有点|239434|entityMap^0|4|E|L|F|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]|$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|W|8|@]|D|@]|E|$]]|$1|M|3|N|5|6|7|X|8|@]|D|@]|E|$]]|$1|O|3|-4|5|6|7|Y|8|@]|D|@]|E|$]]]|P|$]]

You have to import <code>accuracy_score</code> from <code>sklearn.metrics</code>. It should be like below,

<pre><code>from sklearn.metrics import accuracy_score
print accuracy_score(predictions,test set of labels)
</code></pre>

The formula for accuracy is:

Number of points classified correctly / all the points in test set

blocks|key|741406|text|首先，您需要从sklearn导入度量，在度量中您需要导入accuracy_score|type|unstyled|depth|inlineStyleRanges|entityRanges|data|741407|然后你就能得到精确的分数。|741408|accuracy_score公式是|741409|预测的accuracy_score=correct_predictions/No+|offset|length|style|BOLD|741410|from+sklearn.metrics+import+accuracy_score|CODE|741411|accuracy_score(y_actual,y_predicted)|741412|PS。它对分类技术非常有用。|741413|entityMap^0|0|0|0|3|12|0|0|16|0|0|10|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|V|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|X|8|@$H|Y|I|Z|J|K]]|9|@]|A|$]]|$1|L|3|M|5|6|7|10|8|@$H|11|I|12|J|N]]|9|@]|A|$]]|$1|O|3|P|5|6|7|13|8|@$H|14|I|15|J|N]]|9|@]|A|$]]|$1|Q|3|R|5|6|7|16|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|17|8|@]|9|@]|A|$]]]|T|$]]

First you need to import the metrics from sklearn and in metrics you need to import the accuracy_score 
Then you can get the accuracy score
The accuracy_score formula is 
accuracy_score=correct_predictions/No of Predictions
<code>from sklearn.metrics import accuracy_score</code> 
<code>accuracy_score(y_actual,y_predicted)</code>
PS. It works great for classification techniques

blocks|key|239487|text|您可以在score()中直接使用GaussianNB函数。以这种方式，您不需要预测标签，然后计算准确性。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|239488|from+sklearn.naive_bayes+import+GaussianNB
gnb+=+GaussianNB()
gnb+=+gnb.fit(train_data,+train_labels)
score+=+gnb.score(test_data,+test_labels)|code-block|syntax|javascript|239489|entityMap^0|4|7|G|A|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|S|8|@]|D|@]|E|$]]]|L|$]]

You can use <code>score()</code> function in <code>GaussianNB</code> directly. In this way you don't need to predict labels and then calculate accuracy.
<pre><code>from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb = gnb.fit(train_data, train_labels)
score = gnb.score(test_data, test_labels)
</code></pre>

blocks|key|741418|text|您可以始终使用sklearn的度量来获得模型的准确性--您可以使用accuracy_score(test_data,predictions)来获得模型值与实际值之间的差异，除此之外，您还可以在模型metrics.mean_absolute_error(y_test,predictions)中检查平均绝对误差的错误率，检查metrics.mean_squared_error(y_test,+predictions)中的均方误差。等|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|741419|entityMap^0|X|11|2Q|1B|4H|1B|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]|$9|K|A|L|B|C]|$9|M|A|N|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|O|8|@]|D|@]|E|$]]]|G|$]]

You can always use sklearn's metrics to get your model's accuracy you can either use <code>accuracy_score(test_data,predictions)</code> to get the difference between model's values and actual values, apart from this you can check for error rates in model <code>metrics.mean_absolute_error(y_test,predictions)</code> for mean absolute errors, <code>metrics.mean_squared_error(y_test, predictions)</code> for mean squared errors. etc

blocks|key|739582|text|对于分类问题，使用"metrics.accuracy_score“和回归使用"metrics.r2_score”。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|739583|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

For Classification problems use "metrics.accuracy_score" and Regression use "metrics.r2_score".

I've wondered if there is a function in sklearn which corresponds to the accuracy(difference between actual and predicted data) and how to print it out?

<pre><code>from sklearn import datasets 
iris = datasets.load_iris()
from sklearn.naive_bayes import GaussianNB
naive_classifier= GaussianNB()
y =naive_classifier.fit(iris.data, iris.target).predict(iris.data)
pr=naive_classifier.predict(iris.data)
</code></pre>

How to find out the accuracy?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我想知道sklearn中是否有对应于准确性(实际数据和预测数据之间的差异)的函数，以及如何打印出来？from sklearn import datasets iris = datasets.load_iris()from sklearn.naive_bayes import GaussianNBnaive_classifier= GaussianNB()y =naive_classifier.fi

问如何找出精确性？
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