I got two strange cross-validation scores even I tried different parameter
of random_state in KFold, the last fold significantly lower than other
folds like this:
[0.66555285540704734,
0.64459295261239369,
0.64611178614823817,
0.6488456865127582,
0.65268915223336377,
0.65603160133697969,
0.66423579459130966,
0.097538742023700997]
[0.82442284325637905,
0.8353584447144593,
0.82685297691373028,
0.82320777642770349,
0.82685297691373028,
0.82989064398541923,
0.82006079027355627,
0.64133738601823709]
My code is below
pipe = Pipeline([
('classifier', RandomForestClassifier(n_estimators=100,
min_samples_leaf=10,random_state=random.seed(1234)))
])
clfs = [ (pipe.fit(x[train_index], y[train_index]), (x[test_index],
y[test_index])) for
train_index, test_index in KFold(x.shape[0], n_folds=8,
shuffle=True, random_state=random.seed(125))]
scores = [m.accuracy_score(p[1][1], p[0].predict(p[1][0])) for p in clfs]
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