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Diffstat (limited to 'docs/mllib-linear-methods.md')
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1 files changed, 7 insertions, 3 deletions
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index bc914a1899..44b7f67c57 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -110,12 +110,16 @@ However, L1 regularization can help promote sparsity in weights leading to small It is not recommended to train models without any regularization, especially when the number of training examples is small. +### Optimization + +Under the hood, linear methods use convex optimization methods to optimize the objective functions. MLlib uses two methods, SGD and L-BFGS, described in the [optimization section](mllib-optimization.html). Currently, most algorithm APIs support Stochastic Gradient Descent (SGD), and a few support L-BFGS. Refer to [this optimization section](mllib-optimization.html#Choosing-an-Optimization-Method) for guidelines on choosing between optimization methods. + ## Binary classification [Binary classification](http://en.wikipedia.org/wiki/Binary_classification) aims to divide items into two categories: positive and negative. MLlib -supports two linear methods for binary classification: linear support vector -machines (SVMs) and logistic regression. For both methods, MLlib supports +supports two linear methods for binary classification: linear Support Vector +Machines (SVMs) and logistic regression. For both methods, MLlib supports L1 and L2 regularized variants. The training data set is represented by an RDD of [LabeledPoint](mllib-data-types.html) in MLlib. Note that, in the mathematical formulation in this guide, a training label $y$ is denoted as @@ -123,7 +127,7 @@ either $+1$ (positive) or $-1$ (negative), which is convenient for the formulation. *However*, the negative label is represented by $0$ in MLlib instead of $-1$, to be consistent with multiclass labeling. -### Linear support vector machines (SVMs) +### Linear Support Vector Machines (SVMs) The [linear SVM](http://en.wikipedia.org/wiki/Support_vector_machine#Linear_SVM) is a standard method for large-scale classification tasks. It is a linear method as described above in equation `$\eqref{eq:regPrimal}$`, with the loss function in the formulation given by the hinge loss: |