70 lines
2.7 KiB
Plaintext
70 lines
2.7 KiB
Plaintext
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tag::dependent_variable[]
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`dependent_variable`::
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(Required, string) Defines which field of the document is to be predicted.
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This parameter is supplied by field name and must match one of the fields in
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the index being used to train. If this field is missing from a document, then
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that document will not be used for training, but a prediction with the trained
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model will be generated for it. It is also known as continuous target variable.
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end::dependent_variable[]
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tag::eta[]
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`eta`::
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(Optional, double) The shrinkage applied to the weights. Smaller values result
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in larger forests which have better generalization error. However, the smaller
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the value the longer the training will take. For more information, see
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https://en.wikipedia.org/wiki/Gradient_boosting#Shrinkage[this wiki article]
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about shrinkage.
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end::eta[]
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tag::feature_bag_fraction[]
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`feature_bag_fraction`::
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(Optional, double) Defines the fraction of features that will be used when
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selecting a random bag for each candidate split.
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end::feature_bag_fraction[]
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tag::gamma[]
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`gamma`::
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(Optional, double) Regularization parameter to prevent overfitting on the
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training dataset. Multiplies a linear penalty associated with the size of
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individual trees in the forest. The higher the value the more training will
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prefer smaller trees. The smaller this parameter the larger individual trees
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will be and the longer train will take.
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end::gamma[]
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tag::lambda[]
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`lambda`::
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(Optional, double) Regularization parameter to prevent overfitting on the
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training dataset. Multiplies an L2 regularisation term which applies to leaf
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weights of the individual trees in the forest. The higher the value the more
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training will attempt to keep leaf weights small. This makes the prediction
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function smoother at the expense of potentially not being able to capture
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relevant relationships between the features and the {depvar}. The smaller this
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parameter the larger individual trees will be and the longer train will take.
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end::lambda[]
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tag::maximum_number_trees[]
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`maximum_number_trees`::
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(Optional, integer) Defines the maximum number of trees the forest is allowed
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to contain. The maximum value is 2000.
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end::maximum_number_trees[]
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tag::prediction_field_name[]
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`prediction_field_name`::
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(Optional, string) Defines the name of the prediction field in the results.
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Defaults to `<dependent_variable>_prediction`.
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end::prediction_field_name[]
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tag::training_percent[]
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`training_percent`::
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(Optional, integer) Defines what percentage of the eligible documents that will
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be used for training. Documents that are ignored by the analysis (for example
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those that contain arrays) won’t be included in the calculation for used
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percentage. Defaults to `100`.
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end::training_percent[]
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