How is logistic regression parallelized in Spark?

Question

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。

Answer 1

Spark uses a so called mini batch gradient descent for regression:

http://ruder.io/optimizing-gradient-descent/index.html#minibatchgradientdescent

In a nutshell, it works like this:

1. Select a sample of the data
2. Compute the gradient on each row of the sample
3. Aggregate the gradient
4. Back to step 1

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