Dear SciKitters,
given a dataset (2200 sample, 90 features), I want to train a RF but run
into an interesting issue.
My array containing the labels (dataActs_array) only shows 2 classes:
"
from collections import defaultdict
d = defaultdict(int)
for elt in dataActs_array:
d[elt] += 1
print d
defaultdict(<type 'int'>, {'1': 1505, '0': 774})
"
However, after splitting into test and train:
"
sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test = train_test_split
(dataDescrs_array,dataActs_array,test_size=.4)
"
the confusion matrix outputs 4 classes:
"
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
clf_RF = RandomForestClassifier()
clf_RF = clf_RF.fit(X_train,y_train)
y_predict = clf_RF.predict(X_test)
print metrics.confusion_matrix(y_test,y_predict)
[[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]]
"
Where have all my classes gone?
Why do I end up in a 4*4 array?
Cheers & Thanks,
Paul
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