evalml.pipelines.CatBoostBinaryClassificationPipeline

class evalml.pipelines.CatBoostBinaryClassificationPipeline(parameters, random_state=0)

CatBoost Pipeline for binary classification. CatBoost is an open-source library and natively supports categorical features.

For more information, check out https://catboost.ai/ Note: impute_strategy must support both string and numeric data

name = 'Cat Boost Binary Classification Pipeline'

custom_name = None

summary = 'CatBoost Classifier w/ Simple Imputer'

component_graph = ['Simple Imputer', 'CatBoost Classifier']

problem_type = 'binary'

model_family = 'catboost'

hyperparameters = {'CatBoost Classifier': {'eta': Real(low=1e-06, high=1, prior='uniform', transform='identity'), 'max_depth': Integer(low=4, high=10, prior='uniform', transform='identity'), 'n_estimators': Integer(low=10, high=1000, prior='uniform', transform='identity')}, 'Simple Imputer': {'impute_strategy': ['most_frequent']}}

custom_hyperparameters = {'Simple Imputer': {'impute_strategy': ['most_frequent']}}

default_parameters = {'CatBoost Classifier': {'bootstrap_type': None, 'eta': 0.03, 'max_depth': 6, 'n_estimators': 1000}, 'Simple Imputer': {'fill_value': None, 'impute_strategy': 'most_frequent'}}

Instance attributes

feature_importance	Return importance associated with each feature.
parameters	Returns parameter dictionary for this pipeline
threshold

Methods:

__init__	Machine learning pipeline made out of transformers and a estimator.
clone	Constructs a new pipeline with the same parameters and components.
describe	Outputs pipeline details including component parameters
fit	Build a model
get_component	Returns component by name
graph	Generate an image representing the pipeline graph
graph_feature_importance	Generate a bar graph of the pipeline’s feature importance
load	Loads pipeline at file path
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.
save	Saves pipeline at file path
score	Evaluate model performance on objectives