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

AutoClassificationSearch	Automatic pipeline search class for classification problems
AutoRegressionSearch	Automatic pipeline search for regression problems
AutoSearchBase	Base class for AutoML searches.

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.
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 binary 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.

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.

OneHotEncoder	One-hot encoder to encode non-numeric data
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.
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
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.