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 |
Gradient Boosting Tree¶
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 |
Gradient Boosting Tree 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 |