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src/Microsoft.ML.FastTree/FastTreeRegression.cs

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@@ -43,30 +43,11 @@ namespace Microsoft.ML.Trainers.FastTree
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/// | | |
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/// | -- | -- |
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/// | Machine learning task | Regression |
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/// | Is normalization required? | Yes |
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/// | Is normalization required? | No |
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/// | Is caching required? | No |
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/// | Required NuGet in addition to Microsoft.ML | Microsoft.ML.FastTree |
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///
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/// ### Training Algorithm Details
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/// FastTree is an efficient implementation of the [MART](https://arxiv.org/abs/1505.01866) gradient boosting algorithm.
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/// Gradient boosting is a machine learning technique for regression problems.
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/// It builds each regression tree in a step-wise fashion, using a predefined loss function to measure the error for each step and corrects for it in the next.
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/// So this prediction model is actually an ensemble of weaker prediction models.In regression problems, boosting builds a series of such trees in a step-wise fashion and then selects the optimal tree using an arbitrary differentiable loss function.
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///
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/// MART learns an ensemble of regression trees, which is a decision tree with scalar values in its leaves.
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/// A decision(or regression) tree is a binary tree-like flow chart, where at each interior node one decides which of the two child nodes to continue to based on one of the feature values from the input.
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/// At each leaf node, a value is returned.In the interior nodes, the decision is based on the test 'x <= v' where x is the value of the feature in the input sample and v is one of the possible values of this feature.
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/// The functions that can be produced by a regression tree are all the piece-wise constant functions.
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///
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/// The ensemble of trees is produced by computing, in each step, a regression tree that approximates the gradient of the loss function, and adding it to the previous tree with coefficients that minimize the loss of the new tree.
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/// The output of the ensemble produced by MART on a given instance is the sum of the tree outputs.
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/// In case of a binary classification problem, the output is converted to a probability by using some form of calibration.
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/// In case of a regression problem, the output is the predicted value of the function.
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/// In case of a ranking problem, the instances are ordered by the output value of the ensemble.
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///
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/// For more information see:
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/// [Wikipedia: Gradient boosting (Gradient tree boosting)](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting).
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/// [Greedy function approximation: A gradient boosting machine.](https://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451)
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/// [!include[algorithm](~/../docs/samples/docs/api-reference/algo-details-fasttree.md)]
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/// ]]>
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/// </format>
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/// </remarks>

src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs

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public static class TreeExtensions
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{
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/// <summary>
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/// Creates <see cref="FastTreeRegressionTrainer"/>, which predicts a target using a decision tree regression model.
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/// Create <see cref="FastTreeRegressionTrainer"/>, which predicts a target using a decision tree regression model.
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/// </summary>
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/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
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/// <param name="labelColumnName">The name of the label column. The column data must be <see cref="System.Single"/>.</param>
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}
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/// <summary>
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/// Creates <see cref="FastTreeRegressionTrainer"/>, which predicts a target using a decision tree regression model with advanced options.
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/// Create <see cref="FastTreeRegressionTrainer"/> with advanced options, which predicts a target using a decision tree regression model.
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/// </summary>
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/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
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/// <param name="options">Trainer options.</param>

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