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

AutoClassificationSearch	Automatic pipeline search class for classification problems
AutoRegressionSearch	Automatic pipeline search for regression problems

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.
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

Regression Pipelines

RFRegressionPipeline	Random Forest Pipeline for regression problems
CatBoostRegressionPipeline	CatBoost Pipeline for regression problems.
LinearRegressionPipeline	Linear Regression Pipeline for regression problems
XGBoostRegressionPipeline	XGBoost 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 Plot Utils

roc_curve	Receiver Operating Characteristic score for binary classification.
confusion_matrix	Confusion matrix for binary and multiclass classification.
normalize_confusion_matrix	Normalizes a confusion matrix.

Components

Transformers

Encoders

Encoders convert categorical or non-numerical features into numerical features.

OneHotEncoder

One-hot encoder to encode non-numeric data

Imputers

Imputers fill in missing data.

SimpleImputer

Imputes missing data according to a specified imputation strategy

Scalers

Scalers transform and standardize the range of data.

StandardScaler

Standardize features: removes mean and scales to unit variance

Feature Selectors

Feature selectors select a subset of relevant features for the model.

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 models which can be trained to predict a class label from input data.

CatBoostClassifier	CatBoost Classifier, a classifier that uses gradient-boosting on decision trees.
RandomForestClassifier	Random Forest Classifier
LogisticRegressionClassifier	Logistic Regression Classifier
XGBoostClassifier	XGBoost Classifier

Regressors

Regressors are models which can be trained to predict a target value from input data.

CatBoostRegressor	CatBoost Regressor, a regressor that uses gradient-boosting on decision trees.
LinearRegressor	Linear Regressor
RandomForestRegressor	Random Forest Regressor
XGBoostRegressor	XGBoost Regressor

Objective Functions

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
MedianAE	Median absolute error for regression
MaxError	Maximum residual error for regression
ExpVariance	Explained variance score 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

Guardrails

detect_highly_null	Checks if there are any highly-null columns in a dataframe.
detect_label_leakage	Check if any of the features are highly correlated with the target.
detect_outliers	Checks if there are any outliers in a dataframe by using first Isolation Forest to obtain the anomaly score of each index and then using IQR to determine score anomalies.
detect_id_columns	Check if any of the features are ID columns.

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.