Using multiprocessing -Pool- with -sklearn-, code runs but cores dont show any work

I am trying to do some machine learning on about 31 000 rows and 1000 columns. It takes a long time so I thought I could parallelize the job, so I made it into a funciton and try to use the Tool on my Windows 10 with jupyter notebook. But it just works and when I look at my cores on the Task Manager they are not working. Is there something wrong with the code or is it just not supported?

from sklearn.model_selection import train_test_split
X_dev, X_test, y_dev, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

from sklearn.model_selection import KFold
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import Imputer
from sklearn.metrics import accuracy_score
from multiprocessing import Pool
from datetime import datetime as dt

def tree_paralel(x):
    tree = DecisionTreeClassifier(criterion="gini", max_depth= x, random_state=1)  
    accuracy_ = []
    for train_idx, val_idx in kfolds.split(X_dev, y_dev):

        X_train, y_train, = X_dev.iloc[train_idx], y_dev.iloc[train_idx]
        X_val, y_val = X_dev.iloc[val_idx], y_dev.iloc[val_idx] 

        X_train = pd.DataFrame(im.fit_transform(X_train),index = X_train.index)
        X_val = pd.DataFrame(im.transform(X_val), index = X_val.index)
        tree.fit(X_train, y_train)
        y_pred = tree.predict(X_val)
        accuracy_.append(accuracy_score(y_val, y_pred))
    print("This was the "+str(x)+" iteration", (dt.now() - start).total_seconds())
    return accuracy_

And then use the multiprocessing tool:

kfolds = KFold(n_splits=10)
accuracy = []
im = Imputer()

p = Pool(5)

input_ = range(1,11)
output_ = []
start = dt.now()
for result in p.imap(tree_paralel, input_):
    output_.append(result)
p.close()
print("Time:", (dt.now() - start).total_seconds())