evalml.pipelines.CatBoostMulticlassClassificationPipeline

Inheritance diagram of CatBoostMulticlassClassificationPipeline
class evalml.pipelines.CatBoostMulticlassClassificationPipeline(parameters, random_state=0)[source]

CatBoost Pipeline for multiclass 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 Multiclass Classification Pipeline'
custom_name = None
summary = 'CatBoost Classifier w/ Simple Imputer'
component_graph = ['Simple Imputer', 'CatBoost Classifier']
problem_type = 'multiclass'
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

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