API Reference

Demo Datasets

load_fraud	Load credit card fraud dataset.
load_wine	Load wine dataset.
load_breast_cancer	Load breast cancer dataset.
load_diabetes	Load diabetes dataset.

Preprocessing

Utilities to preprocess data before using evalml.

drop_nan_target_rows	Drops rows in X and y when row in the target y has a value of NaN.
label_distribution	Get the label distributions.
load_data	Load features and labels from file.
number_of_features	Get the number of features for specific dtypes.
split_data	Splits data into train and test sets.

AutoML

AutoML Search Classes

AutoMLSearch

Automated Pipeline search.

AutoML Algorithm Classes

AutoMLAlgorithm	Base class for the automl algorithms which power evalml.
IterativeAlgorithm	An automl algorithm which first fits a base round of pipelines with default parameters, then does a round of parameter tuning on each pipeline in order of performance.

Pipelines

Pipeline Base Classes

PipelineBase	Base class for all pipelines.
ClassificationPipeline	Pipeline subclass for all classification pipelines.
BinaryClassificationPipeline	Pipeline subclass for all binary classification pipelines.
MulticlassClassificationPipeline	Pipeline subclass for all multiclass classification pipelines.
RegressionPipeline	Pipeline subclass for all regression pipelines.

Classification Pipelines

CatBoostBinaryClassificationPipeline	CatBoost Pipeline for binary classification.
CatBoostMulticlassClassificationPipeline	CatBoost Pipeline for multiclass classification.
ENBinaryPipeline	Elastic Net Pipeline for binary classification problems.
ENMulticlassPipeline	Elastic Net Pipeline for multiclass classification problems.
ETBinaryClassificationPipeline	Extra Trees Pipeline for binary classification.
ETMulticlassClassificationPipeline	Extra Trees Pipeline for multiclass classification.
LogisticRegressionBinaryPipeline	Logistic Regression Pipeline for binary classification.
LogisticRegressionMulticlassPipeline	Logistic Regression Pipeline for multiclass classification.
RFBinaryClassificationPipeline	Random Forest Pipeline for binary classification.
RFMulticlassClassificationPipeline	Random Forest Pipeline for multiclass classification.
XGBoostBinaryPipeline	XGBoost Pipeline for binary classification.
XGBoostMulticlassPipeline	XGBoost Pipeline for multiclass classification.
BaselineBinaryPipeline	Baseline Pipeline for binary classification.
BaselineMulticlassPipeline	Baseline Pipeline for multiclass classification.
ModeBaselineBinaryPipeline	Mode Baseline Pipeline for binary classification.
ModeBaselineMulticlassPipeline	Mode Baseline Pipeline for multiclass classification.

Regression Pipelines

RFRegressionPipeline	Random Forest Pipeline for regression problems.
CatBoostRegressionPipeline	CatBoost Pipeline for regression problems.
ENRegressionPipeline	Elastic Net Pipeline for regression problems.
ETRegressionPipeline	Extra Trees Pipeline for regression problems.
LinearRegressionPipeline	Linear Regression Pipeline for regression problems.
XGBoostRegressionPipeline	XGBoost Pipeline for regression problems.
BaselineRegressionPipeline	Baseline Pipeline for regression problems.
MeanBaselineRegressionPipeline	Baseline Pipeline for regression problems.

Pipeline Utils

all_pipelines	Returns a complete list of all supported pipeline classes.
get_pipelines	Returns the pipelines allowed for a particular problem type.
get_estimators	Returns the estimators allowed for a particular problem type.
make_pipeline	Given input data, target data, an estimator class and the problem type,
list_model_families	List model type for a particular problem type.

Pipeline Graph Utils

precision_recall_curve	Given labels and binary classifier predicted probabilities, compute and return the data representing a precision-recall curve.
graph_precision_recall_curve	Generate and display a precision-recall plot.
roc_curve	Given labels and classifier predicted probabilities, compute and return the data representing a Receiver Operating Characteristic (ROC) curve.
graph_roc_curve	Generate and display a Receiver Operating Characteristic (ROC) plot.
confusion_matrix	Confusion matrix for binary and multiclass classification.
normalize_confusion_matrix	Normalizes a confusion matrix.
graph_confusion_matrix	Generate and display a confusion matrix plot.
calculate_permutation_importance	Calculates permutation importance for features.
graph_permutation_importance	Generate a bar graph of the pipeline’s permutation importance.

Components

Component Base Classes

Components represent a step in a pipeline.

ComponentBase	Base class for all components.
Transformer	A component that may or may not need fitting that transforms data.
Estimator	A component that fits and predicts given data.

Transformers

Transformers are components that take in data as input and output transformed data.

DropColumns	Drops specified columns in input data.
SelectColumns	Selects specified columns in input data.
OneHotEncoder	One-hot encoder to encode non-numeric data.
PerColumnImputer	Imputes missing data according to a specified imputation strategy per column
SimpleImputer	Imputes missing data according to a specified imputation strategy.
StandardScaler	Standardize features: removes mean and scales to unit variance.
RFRegressorSelectFromModel	Selects top features based on importance weights using a Random Forest regressor.
RFClassifierSelectFromModel	Selects top features based on importance weights using a Random Forest classifier.

Estimators

Classifiers

Classifiers are components that output a predicted class label.

CatBoostClassifier	CatBoost Classifier, a classifier that uses gradient-boosting on decision trees.
ElasticNetClassifier	Elastic Net Classifier.
ExtraTreesClassifier	Extra Trees Classifier.
RandomForestClassifier	Random Forest Classifier.
LogisticRegressionClassifier	Logistic Regression Classifier.
XGBoostClassifier	XGBoost Classifier.
BaselineClassifier	Classifier that predicts using the specified strategy.

Regressors

Regressors are components that output a predicted target value.

CatBoostRegressor	CatBoost Regressor, a regressor that uses gradient-boosting on decision trees.
ElasticNetRegressor	Elastic Net Regressor.
LinearRegressor	Linear Regressor.
ExtraTreesRegressor	Extra Trees Regressor.
RandomForestRegressor	Random Forest Regressor.
XGBoostRegressor	XGBoost Regressor.
BaselineRegressor	Regressor that predicts using the specified strategy.

Objective Functions

Objective Base Classes

ObjectiveBase	Base class for all objectives.
BinaryClassificationObjective	Base class for all binary classification objectives.
MulticlassClassificationObjective	Base class for all multiclass classification objectives.
RegressionObjective	Base class for all regression objectives.

Domain-Specific Objectives

FraudCost	Score the percentage of money lost of the total transaction amount process due to fraud.
LeadScoring	Lead scoring.

Classification Objectives

AccuracyBinary	Accuracy score for binary classification.
AccuracyMulticlass	Accuracy score for multiclass classification.
AUC	AUC score for binary classification.
AUCMacro	AUC score for multiclass classification using macro averaging.
AUCMicro	AUC score for multiclass classification using micro averaging.
AUCWeighted	AUC Score for multiclass classification using weighted averaging.
BalancedAccuracyBinary	Balanced accuracy score for binary classification.
BalancedAccuracyMulticlass	Balanced accuracy score for multiclass classification.
F1	F1 score for binary classification.
F1Micro	F1 score for multiclass classification using micro averaging.
F1Macro	F1 score for multiclass classification using macro averaging.
F1Weighted	F1 score for multiclass classification using weighted averaging.
LogLossBinary	Log Loss for binary classification.
LogLossMulticlass	Log Loss for multiclass classification.
MCCBinary	Matthews correlation coefficient for binary classification.
MCCMulticlass	Matthews correlation coefficient for multiclass classification.
Precision	Precision score for binary classification.
PrecisionMicro	Precision score for multiclass classification using micro averaging.
PrecisionMacro	Precision score for multiclass classification using macro averaging.
PrecisionWeighted	Precision score for multiclass classification using weighted averaging.
Recall	Recall score for binary classification.
RecallMicro	Recall score for multiclass classification using micro averaging.
RecallMacro	Recall score for multiclass classification using macro averaging.
RecallWeighted	Recall score for multiclass classification using weighted averaging.

Regression Objectives

R2	Coefficient of determination for regression.
MAE	Mean absolute error for regression.
MSE	Mean squared error for regression.
MeanSquaredLogError	Mean squared log error for regression.
MedianAE	Median absolute error for regression.
MaxError	Maximum residual error for regression.
ExpVariance	Explained variance score for regression.
RootMeanSquaredError	Root mean squared error for regression.
RootMeanSquaredLogError	Root mean squared log error for regression.

Problem Types

ProblemTypes

Enum for type of machine learning problem: BINARY, MULTICLASS, or REGRESSION.

handle_problem_types

Handles problem_type by either returning the ProblemTypes or converting from a str.

Model Family

ModelFamily

Enum for family of machine learning models.

Tuners

Tuner	Defines API for Tuners.
SKOptTuner	Bayesian Optimizer.
GridSearchTuner	Grid Search Optimizer.
RandomSearchTuner	Random Search Optimizer.

Data Checks

Data Check Classes

DataCheck	Base class for all data checks.
InvalidTargetDataCheck	Checks if the target labels contain missing or invalid data.
HighlyNullDataCheck	Checks if there are any highly-null columns in the input.
IDColumnsDataCheck	Check if any of the features are likely to be ID columns.
LabelLeakageDataCheck	Check if any of the features are highly correlated with the target.
OutliersDataCheck	Checks if there are any outliers in input data by using an Isolation Forest to obtain the anomaly score of each index and then using IQR to determine score anomalies.

DataChecks	A collection of data checks.
DefaultDataChecks	A collection of basic data checks that is used by AutoML by default.

Data Check Messages

DataCheckMessage	Base class for all DataCheckMessages.
DataCheckError	DataCheckMessage subclass for errors returned by data checks.
DataCheckWarning	DataCheckMessage subclass for warnings returned by data checks.

Data Check Message Types

DataCheckMessageType

Enum for type of data check message: WARNING or ERROR.

Utils

import_or_raise	Attempts to import the requested library by name.
convert_to_seconds	Converts a string describing a length of time to its length in seconds.
get_random_state	Generates a numpy.random.RandomState instance using seed.
get_random_seed	Given a numpy.random.RandomState object, generate an int representing a seed value for another random number generator.