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.

Preprocessing

load_data	Load features and labels from file(s).
split_data	Splits data into train and test sets.

AutoML

AutoClassificationSearch	Automatic pipeline search class for classification problems
AutoRegressionSearch	Automatic pipeline search for regression problems

Plotting

AutoClassificationSearch.plot.get_roc_data	Gets data that can be used to create a ROC plot.
AutoClassificationSearch.plot.generate_roc_plot	Generate Receiver Operating Characteristic (ROC) plot for a given pipeline using cross-validation using the data returned from get_roc_data().
AutoClassificationSearch.plot.get_confusion_matrix_data	Gets data that can be used to create a confusion matrix plot.
AutoClassificationSearch.plot.generate_confusion_matrix	Generate confusion matrix plot for a given pipeline using the data returned from get_confusion_matrix_data().
AutoClassificationSearch.plot.generate_confusion_matrix	Generate confusion matrix plot for a given pipeline using the data returned from get_confusion_matrix_data().

Model Family

ModelFamily

Enum for family of machine learning models.

Components

Transformers

OneHotEncoder	One-hot encoder to encode non-numeric data
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
SimpleImputer	Imputes missing data according to a specified imputation strategy
StandardScaler	Standardize features: removes mean and scales to unit variance

Estimators

LogisticRegressionClassifier	Logistic Regression Classifier
RandomForestClassifier	Random Forest Classifier
XGBoostClassifier	XGBoost Classifier
LinearRegressor	Linear Regressor
RandomForestRegressor	Random Forest Regressor

Pipelines

Pipelines

PipelineBase

Base class for all pipelines.

RFClassificationPipeline	Random Forest Pipeline for both binary and multiclass classification
XGBoostPipeline	XGBoost Pipeline for both binary and multiclass classification
CatBoostClassificationPipeline	CatBoost Pipeline for both binary and multiclass classification.
LogisticRegressionPipeline	Logistic Regression Pipeline for both binary and multiclass classification
RFRegressionPipeline	Random Forest Pipeline for regression problems
CatBoostRegressionPipeline	CatBoost Pipeline for regression problems.
LinearRegressionPipeline	Linear Regression Pipeline for regression problems

Pipeline Utils

get_pipelines	Returns the pipelines allowed for a particular problem type.
list_model_families	List model type for a particular problem type

Plotting

PipelineBase.graph([filepath])	Generate an image representing the pipeline graph
PipelineBase.feature_importance_graph([…])	Generate a bar graph of the pipeline’s feature importances

Objective Functions

Domain Specific

FraudCost	Score the percentage of money lost of the total transaction amount process due to fraud
LeadScoring	Lead scoring

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
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
AUC	AUC score for binary classification
AUCMicro	AUC score for multiclass classification using micro averaging
AUCMacro	AUC score for multiclass classification using macro averaging
AUCWeighted	AUC Score for multiclass classification using weighted averaging
LogLoss	Log Loss for both binary and multiclass classification
MCC	Matthews correlation coefficient for both binary and multiclass classification
ROC	Receiver Operating Characteristic score for binary classification.
ConfusionMatrix	Confusion matrix for classification problems

Regression

R2	Coefficient of determination for regression
MAE	Mean absolute error for regression
MSE	Mean squared error for regression
MSLE	Mean squared log error for regression
MedianAE	Median absolute error for regression
MaxError	Maximum residual error for regression
ExpVariance	Explained variance score for regression

Problem Types

ProblemTypes

Enum for type of machine learning problem: BINARY, MULTICLASS, or REGRESSION

handle_problem_types

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

Tuners

Tuner	Defines API for Tuners
SKOptTuner	Bayesian Optimizer
GridSearchTuner	Grid Search Optimizer
RandomSearchTuner	Random Search Optimizer

Guardrails

detect_highly_null	Checks if there are any highly-null columns in a dataframe.
detect_label_leakage	Check if any of the features are highly correlated with the target.
detect_outliers	Checks if there are any outliers in a dataframe by using first Isolation Forest to obtain the anomaly score of each index and then using IQR to determine score anomalies.
detect_id_columns	Check if any of the features are ID columns.