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diff --git a/docs/ml-classification-regression.md b/docs/ml-classification-regression.md index 3d6106b532..7c2437eacd 100644 --- a/docs/ml-classification-regression.md +++ b/docs/ml-classification-regression.md @@ -1,7 +1,7 @@ --- layout: global -title: Classification and regression - spark.ml -displayTitle: Classification and regression - spark.ml +title: Classification and regression +displayTitle: Classification and regression --- @@ -22,37 +22,14 @@ displayTitle: Classification and regression - spark.ml \newcommand{\zero}{\mathbf{0}} \]` +This page covers algorithms for Classification and Regression. It also includes sections +discussing specific classes of algorithms, such as linear methods, trees, and ensembles. + **Table of Contents** * This will become a table of contents (this text will be scraped). {:toc} -In `spark.ml`, we implement popular linear methods such as logistic -regression and linear least squares with $L_1$ or $L_2$ regularization. -Refer to [the linear methods in mllib](mllib-linear-methods.html) for -details about implementation and tuning. We also include a DataFrame API for [Elastic -net](http://en.wikipedia.org/wiki/Elastic_net_regularization), a hybrid -of $L_1$ and $L_2$ regularization proposed in [Zou et al, Regularization -and variable selection via the elastic -net](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf). -Mathematically, it is defined as a convex combination of the $L_1$ and -the $L_2$ regularization terms: -`\[ -\alpha \left( \lambda \|\wv\|_1 \right) + (1-\alpha) \left( \frac{\lambda}{2}\|\wv\|_2^2 \right) , \alpha \in [0, 1], \lambda \geq 0 -\]` -By setting $\alpha$ properly, elastic net contains both $L_1$ and $L_2$ -regularization as special cases. For example, if a [linear -regression](https://en.wikipedia.org/wiki/Linear_regression) model is -trained with the elastic net parameter $\alpha$ set to $1$, it is -equivalent to a -[Lasso](http://en.wikipedia.org/wiki/Least_squares#Lasso_method) model. -On the other hand, if $\alpha$ is set to $0$, the trained model reduces -to a [ridge -regression](http://en.wikipedia.org/wiki/Tikhonov_regularization) model. -We implement Pipelines API for both linear regression and logistic -regression with elastic net regularization. - - # Classification ## Logistic regression @@ -760,7 +737,34 @@ Refer to the [`IsotonicRegression` Python docs](api/python/pyspark.ml.html#pyspa </div> </div> +# Linear methods + +We implement popular linear methods such as logistic +regression and linear least squares with $L_1$ or $L_2$ regularization. +Refer to [the linear methods guide for the RDD-based API](mllib-linear-methods.html) for +details about implementation and tuning; this information is still relevant. +We also include a DataFrame API for [Elastic +net](http://en.wikipedia.org/wiki/Elastic_net_regularization), a hybrid +of $L_1$ and $L_2$ regularization proposed in [Zou et al, Regularization +and variable selection via the elastic +net](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf). +Mathematically, it is defined as a convex combination of the $L_1$ and +the $L_2$ regularization terms: +`\[ +\alpha \left( \lambda \|\wv\|_1 \right) + (1-\alpha) \left( \frac{\lambda}{2}\|\wv\|_2^2 \right) , \alpha \in [0, 1], \lambda \geq 0 +\]` +By setting $\alpha$ properly, elastic net contains both $L_1$ and $L_2$ +regularization as special cases. For example, if a [linear +regression](https://en.wikipedia.org/wiki/Linear_regression) model is +trained with the elastic net parameter $\alpha$ set to $1$, it is +equivalent to a +[Lasso](http://en.wikipedia.org/wiki/Least_squares#Lasso_method) model. +On the other hand, if $\alpha$ is set to $0$, the trained model reduces +to a [ridge +regression](http://en.wikipedia.org/wiki/Tikhonov_regularization) model. +We implement Pipelines API for both linear regression and logistic +regression with elastic net regularization. # Decision trees |