Estimators are one of the stages that are added to a pipeline.
An estimator is typically the last stage in a pipeline.
When you add an estimator, such as a
LinearRegression to a pipeline, the
performs parameter estimation and generates a model. The pipeline
PipelineModel that includes the model.
Gradient Boosting Tree
|Gradient Boosting Tree
GBTree.fit(table, *args, **kwargs)
|Fit function for gradient boosting tree
The model classes are created as a result of adding
an estimator class to a
Pipeline and running
fit method with training data. The pipeline
PipelineModel that includes the model
form of the estimator. For example, if the pipeline
NeuralNetwork estimator, then the
returned pipeline model includes a
Gradient Boosting Tree Model
Logistic Regression Model
Pipelines are a series of transformers followed by an estimator
used to construct a self-contained workflow. When the
score method of the pipeline is executed, each stage of the
pipeline is executed in order. The output of each stage is used
as the input for the next stage. The result is the output from
the last stage.
|Execute a series of transformers and estimators
Pipeline.fit(table, *args, **kwargs)
|Train the models using the stages in the pipeline
Pipeline.transform(table, *args, **kwargs)
|Execute the transformations in this pipeline only
Transformers are used to modify your data sets. Currently, this
includes various forms of imputing missing values in your data sets.
|Impute missing values in a data set
|Perform the imputation on the given data set
Hyperparameter tuning allows you to test various combinations of
model parameters in one workflow. This can be done either on
a single Estimator class instance or a Pipeline.