evalml.pipelines.XGBoostBinaryPipeline

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

XGBoost Pipeline for binary classification.

name = 'XGBoost Binary Classification Pipeline'
custom_name = 'XGBoost Binary Classification Pipeline'
summary = 'XGBoost Classifier w/ Simple Imputer + One Hot Encoder'
component_graph = ['Simple Imputer', 'One Hot Encoder', 'XGBoost Classifier']
problem_type = 'binary'
model_family = 'xgboost'
hyperparameters = {'One Hot Encoder': {}, 'Simple Imputer': {'impute_strategy': ['mean', 'median', 'most_frequent']}, 'XGBoost Classifier': {'eta': Real(low=1e-06, high=1, prior='uniform', transform='identity'), 'max_depth': Integer(low=1, high=10, prior='uniform', transform='identity'), 'min_child_weight': Real(low=1, high=10, prior='uniform', transform='identity'), 'n_estimators': Integer(low=1, high=1000, prior='uniform', transform='identity')}}
custom_hyperparameters = None
default_parameters = {'One Hot Encoder': {'categories': None, 'drop': None, 'handle_missing': 'error', 'handle_unknown': 'ignore', 'top_n': 10}, 'Simple Imputer': {'fill_value': None, 'impute_strategy': 'most_frequent'}, 'XGBoost Classifier': {'eta': 0.1, 'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 100}}

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