Dear all,
I am trying to classify a dataset with a binary target. Number of positive
instances represents only the 3% of the total instances. I have tried
using SVC with neither auto_weight nor sample_weight and the confusion
matrix shows that all instances are classified as negative. However, if I
use either auto_weight:auto or sample_weight(computing the weight of each
instances proportional to the porcentaje of its target) then the confusion
matrix is the other way around(that means, all instances are classified as
positive).
What am I doing wrong?
This is how I have made the calls:
1) with no additional parameters:
SVC(probability=True, max_iter=1000, verbose=5)
2) with class_weight:
SVC(class_weight=Œauto¹, probability=True, max_iter=1000, verbose=5)
3) with sample_weight:
classifier = SVC(probability=True, max_iter=1000, verbose=5)
and later:
sample_weight = np.asarray(compute_sample_weight(np.unique(y_train),
y_train))
classifier.fit(X_train, y_train, sample_weight=sample_weight)
def compute_sample_weight(classes, y_train):
# Find the weight of each class as present in y.
le = LabelEncoder()
y_ind = le.fit_transform(y_train)
if not all(np.in1d(classes, le.classes_)):
raise ValueError("classes should have valid labels that are in y")
# inversely proportional to the number of samples in the class
recip_freq = 1. / np.bincount(y_ind)
weight = recip_freq[le.transform(classes)] / np.mean(recip_freq)
weight_by_class = dict(zip(le.classes_, weight))
y_sample_weight = [weight_by_class[e] for e in y_train]
return y_sample_weight
Thanks.
Z.-
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