API Reference

Demo Datasets

load_breast_cancer	Load breast cancer dataset. Binary classification problem.
load_diabetes	Load diabetes dataset. Used for regression problem.
load_fraud	Load credit card fraud dataset.
load_wine	Load wine dataset. Multiclass problem.
load_churn	Load churn dataset, which can be used for binary classification problems.

Preprocessing

Utilities to preprocess data before using evalml.

load_data	Load features and target from file.
target_distribution	Get the target distributions.
number_of_features	Get the number of features of each specific dtype in a DataFrame.
split_data	Split data into train and test sets.

Exceptions

MethodPropertyNotFoundError	Exception to raise when a class is does not have an expected method or property.
PipelineNotFoundError	An exception raised when a particular pipeline is not found in automl search results.
ObjectiveNotFoundError	Exception to raise when specified objective does not exist.
MissingComponentError	An exception raised when a component is not found in all_components().
ComponentNotYetFittedError	An exception to be raised when predict/predict_proba/transform is called on a component without fitting first.
PipelineNotYetFittedError	An exception to be raised when predict/predict_proba/transform is called on a pipeline without fitting first.
AutoMLSearchException	Exception raised when all pipelines in an automl batch return a score of NaN for the primary objective.
PipelineScoreError	An exception raised when a pipeline errors while scoring any objective in a list of objectives.
DataCheckInitError	Exception raised when a data check can’t initialize with the parameters given.
NullsInColumnWarning	Warning thrown when there are null values in the column of interest.

AutoML

AutoML Search Interface

AutoMLSearch

Automated Pipeline search.

AutoML Utils

search	Given data and configuration, run an automl search.
get_default_primary_search_objective	Get the default primary search objective for a problem type.
make_data_splitter	Given the training data and ML problem parameters, compute a data splitting method to use during AutoML 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.

AutoML Callbacks

silent_error_callback	No-op.
log_error_callback	Logs the exception thrown as an error.
raise_error_callback	Raises the exception thrown by the AutoMLSearch object.

AutoML Engines

SequentialEngine	The default engine for the AutoML search.
CFEngine	The concurrent.futures (CF) engine.
DaskEngine	The dask engine.

Pipelines

Pipeline Base Classes

PipelineBase	Machine learning pipeline.
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.
TimeSeriesClassificationPipeline	Pipeline base class for time series classification problems.
TimeSeriesBinaryClassificationPipeline	Pipeline base class for time series binary classification problems.
TimeSeriesMulticlassClassificationPipeline	Pipeline base class for time series multiclass classification problems.
TimeSeriesRegressionPipeline	Pipeline base class for time series regression problems.

Pipeline Utils

make_pipeline	Given input data, target data, an estimator class and the problem type, generates a pipeline class with a preprocessing chain which was recommended based on the inputs. The pipeline will be a subclass of the appropriate pipeline base class for the specified problem_type.
generate_pipeline_code	Creates and returns a string that contains the Python imports and code required for running the EvalML pipeline.
rows_of_interest	Get the row indices of the data that are closest to the threshold. Works only for binary classification problems and pipelines.

Component Graphs

ComponentGraph

Component graph for a pipeline as a directed acyclic graph (DAG).

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. These components are used before an estimator.
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.
generate_component_code	Creates and returns a string that contains the Python imports and code required for running the EvalML component.

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.
SelectByType	Selects columns by specified Woodwork logical type or semantic tag in input data.
OneHotEncoder	A transformer that encodes categorical features in a one-hot numeric array.
TargetEncoder	A transformer that encodes categorical features into target encodings.
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	A transformer that standardizes input features by removing the mean and scaling 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 extract features from datetime columns.
NaturalLanguageFeaturizer	Transformer that can automatically featurize text columns using featuretools’ nlp_primitives.
TimeSeriesFeaturizer	Transformer that delays input features and target variable for time series problems.
DFSTransformer	Featuretools DFS component that generates features for the input features.
PolynomialDetrender	Removes trends from time series by fitting a polynomial to the data.
Undersampler	Initializes an undersampling transformer to downsample the majority classes in the dataset.
Oversampler	SMOTE Oversampler component. Will automatically select whether to use SMOTE, SMOTEN, or SMOTENC based on inputs to the component.

Estimators

Classifiers

Classifiers are components that output a predicted class label.

CatBoostClassifier	CatBoost Classifier, a classifier that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features.
ElasticNetClassifier	Elastic Net Classifier. Uses Logistic Regression with elasticnet penalty as the base estimator.
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.
KNeighborsClassifier	K-Nearest Neighbors Classifier.
SVMClassifier	Support Vector Machine Classifier.
VowpalWabbitBinaryClassifier	Vowpal Wabbit Binary Classifier.
VowpalWabbitMulticlassClassifier	Vowpal Wabbit Multiclass Classifier.

Regressors

Regressors are components that output a predicted target value.

ARIMARegressor	Autoregressive Integrated Moving Average Model. The three parameters (p, d, q) are the AR order, the degree of differencing, and the MA order. More information here: https://www.statsmodels.org/devel/generated/statsmodels.tsa.arima_model.ARIMA.html.
CatBoostRegressor	CatBoost Regressor, a regressor that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features.
ElasticNetRegressor	Elastic Net Regressor.
ExponentialSmoothingRegressor	Holt-Winters Exponential Smoothing Forecaster.
LinearRegressor	Linear Regressor.
ExtraTreesRegressor	Extra Trees Regressor.
RandomForestRegressor	Random Forest Regressor.
XGBoostRegressor	XGBoost Regressor.
BaselineRegressor	Baseline regressor that uses a simple strategy to make predictions. This is useful as a simple baseline regressor to compare with other regressors.
TimeSeriesBaselineEstimator	Time series estimator that predicts using the naive forecasting approach.
StackedEnsembleRegressor	Stacked Ensemble Regressor.
DecisionTreeRegressor	Decision Tree Regressor.
LightGBMRegressor	LightGBM Regressor.
SVMRegressor	Support Vector Machine Regressor.
VowpalWabbitRegressor	Vowpal Wabbit Regressor.

Model Understanding

Utility Methods

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.
roc_curve	Given labels and classifier predicted probabilities, compute and return the data representing a Receiver Operating Characteristic (ROC) curve. Works with binary or multiclass problems.
calculate_permutation_importance	Calculates permutation importance for features.
calculate_permutation_importance_one_column	Calculates permutation importance for one column in the original dataframe.
binary_objective_vs_threshold	Computes objective score as a function of potential binary classification decision thresholds for a fitted binary classification pipeline.
get_prediction_vs_actual_over_time_data	Get the data needed for the prediction_vs_actual_over_time plot.
partial_dependence	Calculates one or two-way partial dependence.
get_prediction_vs_actual_data	Combines y_true and y_pred into a single dataframe and adds a column for outliers. Used in graph_prediction_vs_actual().
get_linear_coefficients	Returns a dataframe showing the features with the greatest predictive power for a linear model.
t_sne	Get the transformed output after fitting X to the embedded space using t-SNE.
find_confusion_matrix_per_thresholds	Gets the confusion matrix and histogram bins for each threshold as well as the best threshold per objective. Only works with Binary Classification Pipelines.

Graph Utility Methods

graph_precision_recall_curve	Generate and display a precision-recall plot.
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.
graph_permutation_importance	Generate a bar graph of the pipeline’s permutation importance.
graph_binary_objective_vs_threshold	Generates a plot graphing objective score vs. decision thresholds for a fitted binary classification pipeline.
graph_prediction_vs_actual	Generate a scatter plot comparing the true and predicted values. Used for regression plotting.
graph_prediction_vs_actual_over_time	Plot the target values and predictions against time on the x-axis.
graph_partial_dependence	Create an one-way or two-way partial dependence plot.
graph_t_sne	Plot high dimensional data into lower dimensional space using t-SNE.

Prediction Explanations

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.

Objectives

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. Scores quantify the benefits of a given value, so greater numeric scores represents a better score. Costs and scores can be negative, indicating that a value is not beneficial. For example, in the case of monetary profit, a negative cost and/or score represents loss of cash flow.

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.
Gini	Gini coefficient for binary classification.
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.
MAPE	Mean absolute percentage error for time series regression. Scaled by 100 to return a percentage.
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

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). Ignores missing and null data.
ProblemTypes	Enum defining the supported types of machine learning problems.

Model Family

handle_model_family	Handles model_family by either returning the ModelFamily or converting from a string.
ModelFamily	Enum for family of machine learning models.

Tuners

Tuner	Base Tuner class.
SKOptTuner	Bayesian Optimizer.
GridSearchTuner	Grid Search Optimizer, which generates all of the possible points to search for using a grid.
RandomSearchTuner	Random Search Optimizer.

Data Checks

Data Check Classes

DataCheck	Base class for all data checks.
InvalidTargetDataCheck	Check if the target data is considered invalid.
NullDataCheck	Check if there are any highly-null numerical, boolean, categorical, natural language, and unknown columns and rows in the input.
IDColumnsDataCheck	Check if any of the features are likely to be ID columns.
TargetLeakageDataCheck	Check if any of the features are highly correlated with the target by using mutual information or Pearson correlation.
OutliersDataCheck	Checks if there are any outliers in input data by using IQR to determine score anomalies.
NoVarianceDataCheck	Check if the target or any of the features have no variance.
ClassImbalanceDataCheck	Check if any of the target labels are imbalanced, or if the number of values for each target are below 2 times the number of CV folds. Use for classification problems.
MulticollinearityDataCheck	Check if any set features are likely to be multicollinear.
DateTimeNaNDataCheck	Check each column in the input for datetime features and will issue an error if NaN values are present.
NaturalLanguageNaNDataCheck	Checks each column in the input for natural language features and will issue an error if NaN values are present.
DateTimeFormatDataCheck	Check if the datetime column has equally spaced intervals and is monotonically increasing or decreasing in order to be supported by time series estimators.
TimeSeriesParametersDataCheck	Checks whether the time series parameters are compatible with data splitting.
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 a message returned by a DataCheck, tagged by name.
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.

Data Check Message Codes

DataCheckMessageCode

Enum for data check message code.

Utils

General Utils

import_or_raise	Attempts to import the requested library by name. If the import fails, raises an ImportError or warning.
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. Or, if given an int, return that int.
pad_with_nans	Pad the beginning num_to_pad rows with nans.
drop_rows_with_nans	Drop rows that have any NaNs in all dataframes or series.
infer_feature_types	Create a Woodwork structure from the given list, pandas, or numpy input, with specified types for columns. If a column’s type is not specified, it will be inferred by Woodwork.
save_plot	Saves fig to filepath if specified, or to a default location if not.
is_all_numeric	Checks if the given DataFrame contains only numeric values.
get_importable_subclasses	Get importable subclasses of a base class. Used to list all of our estimators, transformers, components and pipelines dynamically.

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