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[SPARK-12212][ML][DOC] Clarifies the difference between spark.ml, spark.mllib and mllib in the documentation. #10234
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put the intro again into the ml-guide
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@@ -27,10 +27,10 @@ displayTitle: Classification and regression in spark.ml | |
* This will become a table of contents (this text will be scraped). | ||
{:toc} | ||
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In MLlib, we implement popular linear methods such as logistic | ||
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. In `spark.ml`, we also include Pipelines API for [Elastic | ||
details about implementation and tuning. We also include a Dataframe API for [Elastic | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. "DataFrame" (F capitalized) (elsewhere too) There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Corrected everywhere in the documentation |
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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 | ||
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@@ -523,7 +523,7 @@ feature scaling, and are able to capture non-linearities and feature interaction | |
algorithms such as random forests and boosting are among the top performers for classification and | ||
regression tasks. | ||
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MLlib supports decision trees for binary and multiclass classification and for regression, | ||
The `spark.ml` implementation supports decision trees for binary and multiclass classification and for regression, | ||
using both continuous and categorical features. The implementation partitions data by rows, | ||
allowing distributed training with millions or even billions of instances. | ||
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@@ -611,24 +611,25 @@ All output columns are optional; to exclude an output column, set its correspond | |
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# Tree Ensembles | ||
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The Pipelines API supports two major tree ensemble algorithms: [Random Forests](http://en.wikipedia.org/wiki/Random_forest) and [Gradient-Boosted Trees (GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting). | ||
Both use [MLlib decision trees](ml-decision-tree.html) as their base models. | ||
The Dataframe API supports two major tree ensemble algorithms: [Random Forests](http://en.wikipedia.org/wiki/Random_forest) and [Gradient-Boosted Trees (GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting). | ||
Both use [`spark.ml` decision trees](ml-classification-regression.html#decision-trees) as their base models. | ||
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Users can find more information about ensemble algorithms in the [MLlib Ensemble guide](mllib-ensembles.html). In this section, we demonstrate the Pipelines API for ensembles. | ||
Users can find more information about ensemble algorithms in the [MLlib Ensemble guide](mllib-ensembles.html). | ||
In this section, we demonstrate the Dataframe API for ensembles. | ||
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The main differences between this API and the [original MLlib ensembles API](mllib-ensembles.html) are: | ||
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* support for ML Pipelines | ||
* support for Dataframes and ML Pipelines | ||
* separation of classification vs. regression | ||
* use of DataFrame metadata to distinguish continuous and categorical features | ||
* a bit more functionality for random forests: estimates of feature importance, as well as the predicted probability of each class (a.k.a. class conditional probabilities) for classification. | ||
* more functionality for random forests: estimates of feature importance, as well as the predicted probability of each class (a.k.a. class conditional probabilities) for classification. | ||
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## Random Forests | ||
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[Random forests](http://en.wikipedia.org/wiki/Random_forest) | ||
are ensembles of [decision trees](ml-decision-tree.html). | ||
Random forests combine many decision trees in order to reduce the risk of overfitting. | ||
MLlib supports random forests for binary and multiclass classification and for regression, | ||
The `spark.ml` implementation supports random forests for binary and multiclass classification and for regression, | ||
using both continuous and categorical features. | ||
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For more information on the algorithm itself, please see the [`spark.mllib` documentation on random forests](mllib-ensembles.html). | ||
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@@ -709,7 +710,7 @@ All output columns are optional; to exclude an output column, set its correspond | |
[Gradient-Boosted Trees (GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting) | ||
are ensembles of [decision trees](ml-decision-tree.html). | ||
GBTs iteratively train decision trees in order to minimize a loss function. | ||
MLlib supports GBTs for binary classification and for regression, | ||
The `spark.ml` implementation supports GBTs for binary classification and for regression, | ||
using both continuous and categorical features. | ||
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For more information on the algorithm itself, please see the [`spark.mllib` documentation on GBTs](mllib-ensembles.html). | ||
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spark.mllib right?
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Not this file; the "ml-" prefix ones are for spark.ml. (It's true the functionality is almost the same currently, but it's a bit different and will diverge more.)
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I see the purpose now. It was the old MLlib text, but a lot of it still applies. The distinction is removed.