random.seed returns nothing, and the random module is not used, it is
numpy.random.
You should just pass the integer.
On 12/09/2014 06:50 PM, He-chien Tsai wrote:
Thanks for your approach, I didn't notice that cross_val_score accepts
cross validator as cv
Your approach makes that strange behavior disappeared!
But I still can't figure out what mistake I made, my original code
looks nothing wrong.
BTW, I used pipeline because I planned using data transformation.
2014-12-10 4:33 GMT+08:00 Sebastian Raschka <se.rasc...@gmail.com
<mailto:se.rasc...@gmail.com>>:
What is your dataset size? I am a little bit curious whether you
need the pipe.fit(), I'd do the CV usually like this
clf1 = Pipeline([
('classifier', RandomForestClassifier(n_estimators=100,
min_samples_leaf=10,random_state=random.seed(1234)))
cv = KFold(n=X_train.shape[0],
n_folds=5,
random_state=123)
scores = cross_val_score(clf1, X_train, y_train, cv=cv,
scoring='accuracy')
Best,
Sebastian
> On Dec 9, 2014, at 3:05 PM, He-chien Tsai <depot...@gmail.com
<mailto:depot...@gmail.com>> wrote:
>
> 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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