[英]How is logistic regression parallelized in Spark?
我想对ML库中用于并行化逻辑回归的方法有一些了解,我已经尝试检查源代码,但是我不理解该过程。
Spark uses a so called mini batch gradient descent for regression: Spark使用所谓的迷你批处理梯度下降进行回归:
http://ruder.io/optimizing-gradient-descent/index.html#minibatchgradientdescent http://ruder.io/optimizing-gradient-descent/index.html#minibatchgradientdescent
In a nutshell, it works like this: 简而言之,它的工作方式如下:
The actual optimisation code for Spark is from this line: https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala#L234 Spark的实际优化代码来自以下行: https : //github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala#L234
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