# API Reference¶

## Estimators¶

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 DecisionTree or LinearRegression to a pipeline, the fit method performs parameter estimation and generates a model. The pipeline returns a PipelineModel that includes the model.

### Decision Tree¶

 DecisionTree([alpha, cf_level, criterion, ...]) Decision Tree DecisionTree.fit(table, *args, **kwargs) Fit function for decision tree

### Decision Forest¶

 DecisionForest([alpha, bootstrap, cf_level, ...]) Decision Forest DecisionForest.fit(table, *args, **kwargs) Fit function for decision forest

 GBTree([distribution, ...]) Gradient Boosting Tree GBTree.fit(table, *args, **kwargs) Fit function for gradient boosting tree

### Logistic Regression¶

 LogisticRegression([intercept, max_effects, ...]) Logistic Regression LogisticRegression.fit(table, *args, **kwargs) Fit function for logistic regression

### Linear Regression¶

 LinearRegression([intercept, max_effects, ...]) Linear Regression LinearRegression.fit(table, *args, **kwargs) Fit function for linear regression

### Neural Network¶

 NeuralNetwork([acts, annealing_rate, ...]) Neural Network NeuralNetwork.fit(table, *args, **kwargs) Fit function for neural network

## Models¶

The model classes are created as a result of adding an estimator class to a Pipeline and running the fit method with training data. The pipeline returns a PipelineModel that includes the model form of the estimator. For example, if the pipeline included a NeuralNetwork estimator, then the returned pipeline model includes a NeuralNetworkModel instance.

### Decision Tree Model¶

 DecisionTreeModel.score(table, *args, **kwargs) Score the data using the model DecisionTreeModel.transform(table, *args, ...) Transform function for the model

### Decision Forest Model¶

 DecisionForestModel.score(table, *args, **kwargs) Score the data using the model DecisionForestModel.transform(table, *args, ...) Transform function for the model

 GBTreeModel.score(table, *args, **kwargs) Score the data using the model GBTreeModel.transform(table, *args, **kwargs) Transform function for the model

### Logistic Regression Model¶

 LogisticRegressionModel.score(table, *args, ...) Score the data using the model LogisticRegressionModel.transform(table, ...) Transform function for the model

### Linear Regression Model¶

 LinearRegressionModel.score(table, *args, ...) Score the data using the model LinearRegressionModel.transform(table, ...) Transform function for the model

### Neural Network Model¶

 NeuralNetworkModel.score(table, *args, **kwargs) Score the data using the model NeuralNetworkModel.transform(table, *args, ...) Transform function for the model

## Pipelines¶

### Pipeline¶

Pipelines are a series of transformers followed by an estimator used to construct a self-contained workflow. When the fit or 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.

 Pipeline(stages) 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

### Pipeline Model¶

 PipelineModel.score(table, **kwargs) Apply transformations and score the data using the trained model PipelineModel.transform(table) Run the transforms in the trained pipeline

## Transformers¶

Transformers are used to modify your data sets. Currently, this includes various forms of imputing missing values in your data sets.

### Imputers¶

 Imputer([value]) Impute missing values in a data set Imputer.transform(table[, value]) Perform the imputation on the given data set

## HyperParameter Tuning¶

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.

 HyperParameterTuning(**kwargs) Perform search over all combinations of specified parameters HyperParameterTuning.gridsearch(table[, n_jobs]) Fit model over various permutations of parameters