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.

load_churn

Load credit card fraud 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.

target_distribution

Get the target distributions.

load_data

Load features and target 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 Utils

get_default_primary_search_objective

Get the default primary search objective for a problem type.

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

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

BaselineRegressionPipeline

Baseline Pipeline for regression problems.

MeanBaselineRegressionPipeline

Baseline Pipeline for regression problems.

Pipeline Utils

make_pipeline

Given input data, target data, an estimator class and the problem type,

make_pipeline_from_components

Given a list of component instances and the problem type, an pipeline instance is generated with the component instances.

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.

Component Utils

allowed_model_families

List the model types allowed for a particular problem type.

get_estimators

Returns the estimators allowed for a particular problem type.

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

Imputer

Imputes missing data according to a specified imputation strategy.

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.

DropNullColumns

Transformer to drop features whose percentage of NaN values exceeds a specified threshold

DateTimeFeaturizer

Transformer that can automatically featurize DateTime columns.

TextFeaturizer

Transformer that can automatically featurize text columns.

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.

LightGBMClassifier

LightGBM Classifier

LogisticRegressionClassifier

Logistic Regression Classifier.

XGBoostClassifier

XGBoost Classifier.

BaselineClassifier

Classifier that predicts using the specified strategy.

StackedEnsembleClassifier

Stacked Ensemble Classifier.

DecisionTreeClassifier

Decision Tree Classifier.

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.

StackedEnsembleRegressor

Stacked Ensemble Regressor.

DecisionTreeRegressor

Decision Tree Regressor.

Model Understanding

Graph Utils

confusion_matrix

Confusion matrix for binary and multiclass classification.

normalize_confusion_matrix

Normalizes a confusion matrix.

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 for binary and multiclass classification problems.

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.

binary_objective_vs_threshold

Computes objective score as a function of potential binary classification

graph_binary_objective_vs_threshold

Generates a plot graphing objective score vs.

graph_prediction_vs_actual

Generate a scatter plot comparing the true and predicted values.

Prediction Explanations

explain_prediction

Creates table summarizing the top_k positive and top_k negative contributing features to the prediction of a single datapoint.

explain_predictions

Creates a report summarizing the top contributing features for each data point in the input features.

explain_predictions_best_worst

Creates a report summarizing the top contributing features for the best and worst points in the dataset as measured by error to true labels.

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.

CostBenefitMatrix

Score using a cost-benefit matrix.

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.

Objective Utils

get_all_objective_names

Get a list of the names of all objectives.

get_core_objectives

Returns all core objective instances associated with the given problem type.

get_core_objective_names

Get a list of all valid core objectives.

get_non_core_objectives

Get non-core objective classes.

get_objective

Returns the Objective class corresponding to a given objective name.

Problem Types

ProblemTypes

Enum defining the supported types of machine learning problems.

handle_problem_types

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

detect_problem_type

Determine the type of problem is being solved based on the targets (binary vs multiclass classification, regression)

Model Family

ModelFamily

Enum for family of machine learning models.

handle_model_family

Handles model_family by either returning the ModelFamily or converting from a string

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 data contains missing or invalid values.

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.

NoVarianceDataCheck

Check if the target or any of the features have no variance.

ClassImbalanceDataCheck

Checks if any target labels are imbalanced beyond a threshold.

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

General 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.