API Reference

Demo Datasets

load_breast_cancer	Load breast cancer dataset. Binary classification problem.
load_churn	Load churn dataset, which can be used for binary classification problems.
load_diabetes	Load diabetes dataset. Used for regression problem.
load_fraud	Load credit card fraud dataset.
load_weather	Load the Australian daily-min-termperatures weather dataset.
load_wine	Load wine dataset. Multiclass problem.

Preprocessing

Preprocessing Utils

Utilities to preprocess data before using evalml.

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

Data Splitters

NoSplit	Does not split the training data into training and validation sets.
KFold	Wrapper class for sklearn's KFold splitter.
StratifiedKFold	Wrapper class for sklearn's Stratified KFold splitter.
TrainingValidationSplit	Split the training data into training and validation sets.
TimeSeriesSplit	Rolling Origin Cross Validation for time series problems.

Exceptions

AutoMLSearchException	Exception raised when all pipelines in an automl batch return a score of NaN for the primary objective.
ComponentNotYetFittedError	An exception to be raised when predict/predict_proba/transform is called on a component without fitting first.
DataCheckInitError	Exception raised when a data check can't initialize with the parameters given.
MethodPropertyNotFoundError	Exception to raise when a class is does not have an expected method or property.
MissingComponentError	An exception raised when a component is not found in all_components().
NoPositiveLabelException	Exception when a particular classification label for the 'positive' class cannot be found in the column index or unique values.
ObjectiveCreationError	Exception when get_objective tries to instantiate an objective and required args are not provided.
ObjectiveNotFoundError	Exception to raise when specified objective does not exist.
PartialDependenceError	Exception raised for all errors that partial dependence can raise.
PipelineError	Exception raised for errors that can be raised when applying a pipeline.
PipelineNotFoundError	An exception raised when a particular pipeline is not found in automl search results.
PipelineNotYetFittedError	An exception to be raised when predict/predict_proba/transform is called on a pipeline without fitting first.
PipelineScoreError	An exception raised when a pipeline errors while scoring any objective in a list of objectives.

Warnings

NullsInColumnWarning	Warning thrown when there are null values in the column of interest.
ParameterNotUsedWarning	Warning thrown when a pipeline parameter isn't used in a defined pipeline's component graph during initialization.

Error Codes

PartialDependenceErrorCode	Enum identifying the type of error encountered in partial dependence.
PipelineErrorCodeEnum	Enum identifying the type of error encountered while applying a pipeline.
ValidationErrorCode	Enum identifying the type of error encountered in holdout validation.

AutoML

AutoML Search Interface

AutoMLSearch

Automated Pipeline search.

AutoML Utils

get_default_primary_search_objective	Get the default primary search objective for a problem type.
get_threshold_tuning_info	Determine for a given automl config and pipeline what the threshold tuning objective should be and whether or not training data should be further split to achieve proper threshold tuning.
make_data_splitter	Given the training data and ML problem parameters, compute a data splitting method to use during AutoML search.
resplit_training_data	Further split the training data for a given pipeline. This is needed for binary pipelines in order to properly tune the threshold.
search	Given data and configuration, run an automl search.
search_iterative	Given data and configuration, run an automl search.
tune_binary_threshold	Tunes the threshold of a binary pipeline to the X and y thresholding data.

AutoML Algorithm Classes

AutoMLAlgorithm	Base class for the AutoML algorithms which power EvalML.
DefaultAlgorithm	An automl algorithm that consists of two modes: fast and long, where fast is a subset of long.
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

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

AutoML Engines

CFEngine	The concurrent.futures (CF) engine.
DaskEngine	The dask engine.
EngineBase	Base class for EvalML engines.
SequentialEngine	The default engine for the AutoML search.

Pipelines

Pipeline Base Classes

BinaryClassificationPipeline	Pipeline subclass for all binary classification pipelines.
ClassificationPipeline	Pipeline subclass for all classification pipelines.
MulticlassClassificationPipeline	Pipeline subclass for all multiclass classification pipelines.
PipelineBase	Machine learning pipeline.
RegressionPipeline	Pipeline subclass for all regression pipelines.
TimeSeriesBinaryClassificationPipeline	Pipeline base class for time series binary classification problems.
TimeSeriesClassificationPipeline	Pipeline base class for time series classification problems.
TimeSeriesMulticlassClassificationPipeline	Pipeline base class for time series multiclass classification problems.
TimeSeriesRegressionPipeline	Pipeline base class for time series regression problems.

Pipeline Utils

get_actions_from_option_defaults	Returns a list of actions based on the defaults parameters of each option in the input DataCheckActionOption list.
generate_pipeline_code	Creates and returns a string that contains the Python imports and code required for running the EvalML pipeline.
generate_pipeline_example	Creates and returns a string that contains the Python imports and code required for running the EvalML pipeline.
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.
make_pipeline_from_actions	Creates a pipeline of components to address the input DataCheckAction list.
make_pipeline_from_data_check_output	Creates a pipeline of components to address warnings and errors output from running data checks. Uses all default suggestions.
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.
estimator_unable_to_handle_nans	If True, provided estimator class is unable to handle NaN values as an input.
generate_component_code	Creates and returns a string that contains the Python imports and code required for running the EvalML component.
get_estimators	Returns the estimators allowed for a particular problem type.
handle_component_class	Standardizes input from a string name to a ComponentBase subclass if necessary.
make_balancing_dictionary	Makes dictionary for oversampler components. Find ratio of each class to the majority. If the ratio is smaller than the sampling_ratio, we want to oversample, otherwise, we don't want to sample at all, and we leave the data as is.

Transformers

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

DateTimeFeaturizer	Transformer that can automatically extract features from datetime columns.
DFSTransformer	Featuretools DFS component that generates features for the input features.
DropColumns	Drops specified columns in input data.
DropNaNRowsTransformer	Transformer to drop rows with NaN values.
DropNullColumns	Transformer to drop features whose percentage of NaN values exceeds a specified threshold.
DropRowsTransformer	Transformer to drop rows specified by row indices.
EmailFeaturizer	Transformer that can automatically extract features from emails.
Imputer	Imputes missing data according to a specified imputation strategy.
LabelEncoder	A transformer that encodes target labels using values between 0 and num_classes - 1.
LinearDiscriminantAnalysis	Reduces the number of features by using Linear Discriminant Analysis.
LogTransformer	Applies a log transformation to the target data.
LSA	Transformer to calculate the Latent Semantic Analysis Values of text input.
NaturalLanguageFeaturizer	Transformer that can automatically featurize text columns using featuretools' nlp_primitives.
OneHotEncoder	A transformer that encodes categorical features in a one-hot numeric array.
OrdinalEncoder	A transformer that encodes ordinal features as an array of ordinal integers representing the relative order of categories.
Oversampler	SMOTE Oversampler component. Will automatically select whether to use SMOTE, SMOTEN, or SMOTENC based on inputs to the component.
PCA	Reduces the number of features by using Principal Component Analysis (PCA).
PerColumnImputer	Imputes missing data according to a specified imputation strategy per column.
PolynomialDecomposer	Removes trends and seasonality from time series by fitting a polynomial and moving average to the data.
ReplaceNullableTypes	Transformer to replace features with the new nullable dtypes with a dtype that is compatible in EvalML.
RFClassifierRFESelector	Selects relevant features using recursive feature elimination with a Random Forest Classifier.
RFClassifierSelectFromModel	Selects top features based on importance weights using a Random Forest classifier.
RFRegressorRFESelector	Selects relevant features using recursive feature elimination with a Random Forest Regressor.
RFRegressorSelectFromModel	Selects top features based on importance weights using a Random Forest regressor.
SelectByType	Selects columns by specified Woodwork logical type or semantic tag in input data.
SelectColumns	Selects specified columns in input data.
SimpleImputer	Imputes missing data according to a specified imputation strategy. Natural language columns are ignored.
StandardScaler	A transformer that standardizes input features by removing the mean and scaling to unit variance.
STLDecomposer	Removes trends and seasonality from time series using the STL algorithm.
TargetEncoder	A transformer that encodes categorical features into target encodings.
TargetImputer	Imputes missing target data according to a specified imputation strategy.
TimeSeriesFeaturizer	Transformer that delays input features and target variable for time series problems.
TimeSeriesImputer	Imputes missing data according to a specified timeseries-specific imputation strategy.
TimeSeriesRegularizer	Transformer that regularizes an inconsistently spaced datetime column.
Undersampler	Initializes an undersampling transformer to downsample the majority classes in the dataset.
URLFeaturizer	Transformer that can automatically extract features from URL.

Estimators

Classifiers

Classifiers are components that output a predicted class label.

BaselineClassifier	Classifier that predicts using the specified strategy.
CatBoostClassifier	CatBoost Classifier, a classifier that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features.
DecisionTreeClassifier	Decision Tree Classifier.
ElasticNetClassifier	Elastic Net Classifier. Uses Logistic Regression with elasticnet penalty as the base estimator.
ExtraTreesClassifier	Extra Trees Classifier.
KNeighborsClassifier	K-Nearest Neighbors Classifier.
LightGBMClassifier	LightGBM Classifier.
LogisticRegressionClassifier	Logistic Regression Classifier.
RandomForestClassifier	Random Forest Classifier.
StackedEnsembleClassifier	Stacked Ensemble Classifier.
SVMClassifier	Support Vector Machine Classifier.
VowpalWabbitBinaryClassifier	Vowpal Wabbit Binary Classifier.
VowpalWabbitMulticlassClassifier	Vowpal Wabbit Multiclass Classifier.
XGBoostClassifier	XGBoost 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.
BaselineRegressor	Baseline regressor that uses a simple strategy to make predictions. This is useful as a simple baseline regressor to compare with other regressors.
CatBoostRegressor	CatBoost Regressor, a regressor that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features.
DecisionTreeRegressor	Decision Tree Regressor.
ElasticNetRegressor	Elastic Net Regressor.
ExponentialSmoothingRegressor	Holt-Winters Exponential Smoothing Forecaster.
ExtraTreesRegressor	Extra Trees Regressor.
LightGBMRegressor	LightGBM Regressor.
LinearRegressor	Linear Regressor.
ProphetRegressor	Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
RandomForestRegressor	Random Forest Regressor.
StackedEnsembleRegressor	Stacked Ensemble Regressor.
SVMRegressor	Support Vector Machine Regressor.
TimeSeriesBaselineEstimator	Time series estimator that predicts using the naive forecasting approach.
VowpalWabbitRegressor	Vowpal Wabbit Regressor.
XGBoostRegressor	XGBoost Regressor.

Model Understanding

Metrics

binary_objective_vs_threshold	Computes objective score as a function of potential binary classification decision thresholds for a fitted binary classification pipeline.
calculate_permutation_importance	Calculates permutation importance for features.
calculate_permutation_importance_one_column	Calculates permutation importance for one column in the original dataframe.
confusion_matrix	Confusion matrix for binary and multiclass classification.
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.
get_linear_coefficients	Returns a dataframe showing the features with the greatest predictive power for a linear model.
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_prediction_vs_actual_over_time_data	Get the data needed for the prediction_vs_actual_over_time plot.
normalize_confusion_matrix	Normalizes a confusion matrix.
partial_dependence	Calculates one or two-way partial dependence.
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.
t_sne	Get the transformed output after fitting X to the embedded space using t-SNE.
get_influential_features	Finds the most influential features as well as any detrimental features from a dataframe of feature importances.
readable_explanation	Outputs a human-readable explanation of trained pipeline behavior.

Visualization Methods

graph_binary_objective_vs_threshold	Generates a plot graphing objective score vs. decision thresholds for a fitted binary classification pipeline.
graph_confusion_matrix	Generate and display a confusion matrix plot.
graph_partial_dependence	Create an one-way or two-way partial dependence plot.
graph_permutation_importance	Generate a bar graph of the pipeline's permutation importance.
graph_precision_recall_curve	Generate and display a precision-recall plot.
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_roc_curve	Generate and display a Receiver Operating Characteristic (ROC) plot for binary and multiclass classification problems.
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

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.
FraudCost	Score the percentage of money lost of the total transaction amount process due to fraud.
LeadScoring	Lead scoring.
SensitivityLowAlert	Sensitivity at Low Alert Rates.

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

ExpVariance	Explained variance score 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.
R2	Coefficient of determination 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_default_recommendation_objectives	Get the default recommendation score metrics for the given problem type.
get_non_core_objectives	Get non-core objective classes.
get_objective	Returns the Objective class corresponding to a given objective name.
get_optimization_objectives	Get objectives for optimization.
get_ranking_objectives	Get objectives for pipeline rankings.
normalize_objectives	Converts objectives from a [0, inf) scale to [0, 1] given a max and min for each objective.
organize_objectives	Generate objectives to consider, with optional modifications to the defaults.
ranking_only_objectives	Get ranking-only objective classes.
recommendation_score	Computes a recommendation score for a model given scores for a group of objectives.

Problem Types

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.
handle_problem_types	Handles problem_type by either returning the ProblemTypes or converting from a str.
is_binary	Determines if the provided problem_type is a binary classification problem type.
is_classification	Determines if the provided problem_type is a classification problem type.
is_multiclass	Determines if the provided problem_type is a multiclass classification problem type.
is_regression	Determines if the provided problem_type is a regression problem type.
is_time_series	Determines if the provided problem_type is a time series problem type.
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

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.
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.
IDColumnsDataCheck	Check if any of the features are likely to be ID columns.
InvalidTargetDataCheck	Check if the target data is considered invalid.
MulticollinearityDataCheck	Check if any set features are likely to be multicollinear.
NoVarianceDataCheck	Check if the target or any of the features have no variance.
NullDataCheck	Check if there are any highly-null numerical, boolean, categorical, natural language, and unknown columns and rows in the input.
OutliersDataCheck	Checks if there are any outliers in input data by using IQR to determine score anomalies.
SparsityDataCheck	Check if there are any columns with sparsely populated values in the input.
TargetDistributionDataCheck	Check if the target data contains certain distributions that may need to be transformed prior training to improve model performance. Uses the Shapiro-Wilks test when the dataset is <=5000 samples, otherwise uses Jarque-Bera.
TargetLeakageDataCheck	Check if any of the features are highly correlated with the target by using mutual information, Pearson correlation, and other correlation metrics.
TimeSeriesParametersDataCheck	Checks whether the time series parameters are compatible with data splitting.
TimeSeriesSplittingDataCheck	Checks whether the time series target data is compatible with splitting.
UniquenessDataCheck	Check if there are any columns in the input that are either too unique for classification problems or not unique enough for regression problems.
DataCheck	Base class for all data checks.
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.

Data Check Actions

DataCheckAction	A recommended action returned by a DataCheck.
DataCheckActionCode	Enum for data check action code.
DataCheckActionOption	A recommended action option returned by a DataCheck.

Utils

General Utils

convert_to_seconds	Converts a string describing a length of time to its length in seconds.
downcast_nullable_types	Downcasts IntegerNullable, BooleanNullable types to Double, Boolean in order to support certain estimators like ARIMA, CatBoost, and LightGBM.
drop_rows_with_nans	Drop rows that have any NaNs in all dataframes or series.
get_importable_subclasses	Get importable subclasses of a base class. Used to list all of our estimators, transformers, components and pipelines dynamically.
get_logger	Get the logger with the associated name.
get_time_index	Determines the column in the given data that should be used as the time index.
import_or_raise	Attempts to import the requested library by name. If the import fails, raises an ImportError or warning.
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.
is_all_numeric	Checks if the given DataFrame contains only numeric values.
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.
safe_repr	Convert the given value into a string that can safely be used for repr.
save_plot	Saves fig to filepath if specified, or to a default location if not.