Scikit-Learn Wrapper interface for multi-layer stacking.
mlstacking.sklearn.
StackingModel
(base_models, meta_model, predict_mode='average', n_folds=5, keep_layer_results=True)¶Implementation of the Scikit-Learn API for multi-layer stacking.
Parameters: |
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import numpy
from mlstacking.sklearn import StackingModel
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from xgboost.sklearn import XGBClassifier
X = numpy.random.rand(10,10)
Y = numpy.random.randint(0,2,(10,1))
base_models = [[DecisionTreeClassifier(),RandomForestClassifier(),],
[RandomForestClassifier(),XGBClassifier(),],
[XGBClassifier(),DecisionTreeClassifier(),],]
sm = StackingModel(base_models,XGBClassifier())
sm.fit(X,Y)
sm.predict(X)
# array([0, 0, 0, 1, 0, 0, 0, 1, 1, 1])
sm.predict_proba(X)
# array([[0.6039953 , 0.39600468],
# [0.6039953 , 0.39600468],
# [0.6039953 , 0.39600468],
# [0.40033996, 0.59966004],
# [0.6039953 , 0.39600468],
# [0.6039953 , 0.39600468],
# [0.6039953 , 0.39600468],
# [0.40033996, 0.59966004],
# [0.40033996, 0.59966004],
# [0.40033996, 0.59966004]], dtype=float32)