On 12/13/2014 01:09 AM, He-chien Tsai wrote:
> Thanks for reply. I misused random.seed as it returns None.
> I passed an integer to random_state but it remains that unexpected
> behaviors.
> After I cloned the estimator by sklearn.base.clone,e the result
> becomes reasonable.
>
> clfs = [ (clone(
Thanks for reply. I misused random.seed as it returns None.
I passed an integer to random_state but it remains that unexpected
behaviors.
After I cloned the estimator by sklearn.base.clone,e the result becomes
reasonable.
clfs = [ (clone(pipe).fit(x[train_index], y[train_index]), (x[test_index],
y
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 beha
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 transformati
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_f
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,
0.6
