Package smile.classification

Interface DataFrameClassifier

All Superinterfaces:

Classifier<smile.data.Tuple>, Serializable, ToDoubleFunction<smile.data.Tuple>, ToIntFunction<smile.data.Tuple>

All Known Implementing Classes:

AdaBoost, DecisionTree, GradientTreeBoost, RandomForest

public interface DataFrameClassifier
extends Classifier<smile.data.Tuple>

Classification trait on DataFrame.

Nested Class Summary

Nested Classes

Modifier and Type	Interface	Description
static interface	DataFrameClassifier.Trainer<M extends DataFrameClassifier>	The classifier trainer.

Method Summary

Modifier and Type	Method	Description
static DataFrameClassifier	ensemble(DataFrameClassifier... models)	Return an ensemble of multiple base models to obtain better predictive performance.
smile.data.formula.Formula	formula()	Returns the formula associated with the model.
static DataFrameClassifier	of(smile.data.formula.Formula formula, smile.data.DataFrame data, Properties params, Classifier.Trainer<double[],?> trainer)	Fits a vector classifier on data frame.
default int[]	predict(smile.data.DataFrame data)	Predicts the class labels of a data frame.
default int[]	predict(smile.data.DataFrame data, List<double[]> posteriori)	Predicts the class labels of a dataset.
smile.data.type.StructType	schema()	Returns the predictor schema.

Methods inherited from interface smile.classification.Classifier

applyAsDouble, applyAsInt, classes, numClasses, online, predict, predict, predict, predict, predict, predict, predict, predict, score, soft, update, update, update

Method Details

formula

smile.data.formula.Formula formula()

Returns the formula associated with the model.

Returns:

the formula associated with the model.

schema

smile.data.type.StructType schema()

Returns the predictor schema.

Returns:

the predictor schema.

predict

default int[] predict(smile.data.DataFrame data)

Predicts the class labels of a data frame.

Parameters:

data - the data frame.

Returns:

the predicted class labels.

predict

default int[] predict(smile.data.DataFrame data,
 List<double[]> posteriori)

Predicts the class labels of a dataset.

Parameters:

data - the data frame.

posteriori - an empty list to store a posteriori probabilities on output.

Returns:

the predicted class labels.

of

static DataFrameClassifier of(smile.data.formula.Formula formula,
 smile.data.DataFrame data,
 Properties params,
 Classifier.Trainer<double[],?> trainer)

Fits a vector classifier on data frame.

Parameters:

formula - a symbolic description of the model to be fitted.

data - the data frame of the explanatory and response variables.

params - the hyper-parameters.

trainer - the training lambda.

Returns:

the model.

ensemble

static DataFrameClassifier ensemble(DataFrameClassifier... models)

Return an ensemble of multiple base models to obtain better predictive performance.

Parameters:

models - the base models.

Returns:

the ensemble model.