blocks|key|2836945|text|好吧，它在名字里。SGD分类器是在分类问题中使用SGD+(每次获取每个样本的损失的梯度，并在此过程中更新模型)进行优化(训练)的模型。它可以表示各种分类模型(支持向量机，逻辑回归...)它是用loss参数定义的。默认情况下，它表示线性支持向量机。SGD+Regressor是一个使用SGD进行回归任务优化(训练)的模型。它基本上是一个线性模型，随着学习率的下降而不断更新。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2836946|entityMap^0|2O|4|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

Well, it's in the name. SGD Classifier is a model that is optimized (trained) using SGD (taking the gradient of the loss of each sample at a time and the model is updated along the way) in classification problems. It can represent a variety of classification models (SVM, logistic regression...) which is defined with the <code>loss</code> parameter. By default, it represents linear SVM. SGD Regressor is a model that is optimized (trained) using SGD for regression tasks. It's basically a linear model that is updated along the way with a decaying learning rate.

blocks|key|1405482|text|SGD+(随机梯度下降)是一种机器学习算法或模型用来优化损失函数的优化方法。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1405483|在scikit-learn库中，这些模型是SGDClassifier和SGDRegressor，这可能会让你混淆，认为SGD是一个分类器和回归器。|offset|length|style|CODE|1405484|但事实并非如此。|1405485|SGDClassifier+-它是一个由SGD+SGDRegressor优化的classifier+-它是一个由SGD优化的regressor。|1405486|随机梯度下降{SGD}不支持批量，它与{+batch+}+梯度下降不同，它一次只接受一个训练示例。|BOLD|1405487|使用sklearn部分拟合的示例|1405488|from+sklearn.linear_model+import+SGDClassifier
import+random
clf2+=+SGDClassifier(loss='log')+#+shuffle=True+is+useless+here
shuffledRange+=+range(len(X))
n_iter+=+5
for+n+in+range(n_iter):
++++random.shuffle(shuffledRange)
++++shuffledX+=+[X[i]+for+i+in+shuffledRange]
++++shuffledY+=+[Y[i]+for+i+in+shuffledRange]
++++for+batch+in+batches(range(len(shuffledX)),+10000):
++++++++clf2.partial_fit(shuffledX[batch[0]:batch[-1]%2B1],+shuffledY[batch[0]:batch[-1]%2B1],+classes=numpy.unique(Y))|code-block|syntax|javascript|1405489|entityMap|0|LINK|mutability|MUTABLE|url|https://blog.datarobot.com/classification-with-scikit-learn|1|https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html^0|0|L|D|Z|C|0|0|0|D|O|C|13|A|0|1Q|9|1|0|0|6|T|4|0|0|0^^$0|@$1|2|3|4|5|6|7|15|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|16|8|@$D|17|E|18|F|G]|$D|19|E|1A|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|1B|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|1C|8|@$D|1D|E|1E|F|G]|$D|1F|E|1G|F|G]]|9|@$D|1H|E|1I|1|1J]|$D|1K|E|1L|1|1M]]|A|$]]|$1|L|3|M|5|6|7|1N|8|@$D|1O|E|1P|F|N]|$D|1Q|E|1R|F|N]]|9|@]|A|$]]|$1|O|3|P|5|6|7|1S|8|@]|9|@]|A|$]]|$1|Q|3|R|5|S|7|1T|8|@]|9|@]|A|$T|U]]|$1|V|3|-4|5|6|7|1U|8|@]|9|@]|A|$]]]|W|$X|$5|Y|Z|10|A|$11|12]]|13|$5|Y|Z|10|A|$11|14]]]]

SGD {Stochastic Gradient Descent} is an optimization method, which is used by machine learning algorithms or models to optimize the loss function.

In the scikit-learn library, these model <code>SGDClassifier</code> and <code>SGDRegressor</code>, which might confuse you to think that SGD is a classifier and regressor.

But that's not the case.

<code>SGDClassifier</code> - it is a <a href="https://blog.datarobot.com/classification-with-scikit-learn" rel="nofollow noreferrer">classifier</a> optimized by SGD
<code>SGDRegressor</code> - it is a <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html" rel="nofollow noreferrer">regressor</a> optimized by SGD.

Stochastic gradient descent{SGD} does not support batch, it takes single training example at a time unlike {batch} Gradient descent.

Example using sklearn partial fit

<pre><code>from sklearn.linear_model import SGDClassifier
import random
clf2 = SGDClassifier(loss='log') # shuffle=True is useless here
shuffledRange = range(len(X))
n_iter = 5
for n in range(n_iter):
 random.shuffle(shuffledRange)
 shuffledX = [X[i] for i in shuffledRange]
 shuffledY = [Y[i] for i in shuffledRange]
 for batch in batches(range(len(shuffledX)), 10000):
 clf2.partial_fit(shuffledX[batch[0]:batch[-1]+1], shuffledY[batch[0]:batch[-1]+1], classes=numpy.unique(Y))
</code></pre>

blocks|key|2820076|text|分类器预测某个数据属于哪个类。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2820077|this+picture+is+a+cat+(not+a+dog)|code-block|syntax|javascript|2820078|Regressor通常预测它属于哪个类的概率|2820079|this+picture+with+99%25+of+probability+is+a+cat+|2820080|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

Classifier predicts to which class belongs some data. 

<pre><code>this picture is a cat (not a dog)
</code></pre>

Regressor predicts usually probability to which class it belongs

<pre><code>this picture with 99% of probability is a cat 
</code></pre>

What is difference between SGD classifier and SGD regressor in python sklearn? Also can we set batch size for faster performance in them?

What is difference between SGD classifier and SGD regressor in python?

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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在python sklearn中，SGD分类器和SGD回归器有什么不同？另外，我们可以设置批处理大小以提高性能吗？

问在python中SGD分类器和SGD回归器有什么不同？
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问在python中SGD分类器和SGD回归器有什么不同？EN