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.

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.

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.

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.

FAQ evalml.demos.load_fraud