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.

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

Data Splitter Classes

Data splitter classes for imbalanced classification datasets.

BalancedClassificationDataTVSplit	Data splitter for generating training and validation split using Balanced Classification Data Sampler.
BalancedClassificationDataCVSplit	Data splitter for generating k-fold cross-validation split using Balanced Classification Data Sampler.
KMeansSMOTECVSplit	Splits the data into KFold cross validation sets and balances the training data using K-Means SMOTE.
KMeansSMOTETVSplit	Splits the data into training and validation sets and balances the training data using K-Means SMOTE.
SMOTETomekCVSplit	Splits the data into KFold cross validation sets and uses SMOTE + Tomek links to balance the training data.
SMOTETomekTVSplit	Splits the data into training and validation sets and uses SMOTE + Tomek links balance the training data.
RandomUnderSamplerCVSplit	Splits the training data into KFold cross validation sets and uses RandomUnderSampler to balance the training data.
RandomUnderSamplerTVSplit	Splits the data into training and validation sets and uses RandomUnderSampler to balance the training data.
SMOTENCCVSplit	Splits the data into KFold cross validation sets and uses SMOTENC to balance the training data.
SMOTENCTVSplit	Splits the data into training and validation sets and uses SMOTENC to balance the training data.

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.
IllFormattedClassNameError	Exception to raise when a class name does not comply with EvalML standards
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.
EnsembleMissingPipelinesError	An exception raised when an ensemble is missing estimators (list) as a parameter.
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 Classes

AutoMLSearch

Automated Pipeline search.

AutoML Utils

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.

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.
TimeSeriesClassificationPipeline	Pipeline base class for time series classifcation problems.
TimeSeriesBinaryClassificationPipeline
TimeSeriesMulticlassClassificationPipeline
TimeSeriesRegressionPipeline	Pipeline base class for time series regression problems.

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.
TimeSeriesBaselineRegressionPipeline	Baseline Pipeline for time series 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.
generate_pipeline_code	Creates and returns a string that contains the Python imports and code required for running the EvalML pipeline.

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.
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.
OneHotEncoder	One-hot encoder to encode non-numeric data.
TargetEncoder	Target encoder to encode categorical 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.
DelayedFeatureTransformer	Transformer that delayes input features and target variable for time series problems.
DFSTransformer	Featuretools DFS component that generates features for ww.DataTables and pd.DataFrames
PolynomialDetrender	Removes trends from time series by fitting a polynomial to the data.
Undersampler	Random undersampler component.

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.
KNeighborsClassifier	K-Nearest Neighbors Classifier.
SVMClassifier	Support Vector Machine Classifier.

Regressors

Regressors are components that output a predicted target value.

ARIMARegressor	Autoregressive Integrated Moving Average Model.
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.
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.

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.
calculate_permutation_importance	Calculates permutation importance for features.
binary_objective_vs_threshold	Computes objective score as a function of potential binary classification
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.
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.

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.
graph_prediction_vs_actual	Generate a scatter plot comparing the true and predicted values.
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.

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.
MAPE	Mean absolute percentage error for time series 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

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)
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	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.
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	Checks if any target labels are imbalanced beyond a threshold.
MulticollinearityDataCheck	Check if any set features are likely to be multicollinear.
DateTimeNaNDataCheck	Checks if datetime columns contain NaN values.
NaturalLanguageNaNDataCheck	Checks if natural language columns contain NaN values.

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.

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.
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.
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.
save_plot	Saves fig to filepath if specified, or to a default location if not.
is_all_numeric	Checks if the given DataTable contains only numeric values
get_importable_subclasses	Get importable subclasses of a base class.

FAQ evalml.demos.load_fraud