Hi Patrick,
Yeah, you might be correct. But when I input testing query 'what is where'
with all stopwords, and having filter for stopwords still it classifies it
as :
'what is what' => films => 0.
'what is what' => laptops => 0.
'what is what' => medicine => 0.1667
Dear Sebastian,
Thanks for reply. My actual alpha value was 0.1, I changed it to 0 and
tested the code. But it behave similarly.
On Wed, Sep 3, 2014 at 8:01 PM, Sebastian Raschka
wrote:
> This is due to the Laplace smoothening. If I understand correctly, you
> want the classification to fail if
It's been a bit over a week, so I thought I would try again. I can probably
identify the type of prior by looking at the code. Where exactly is this
prior applied?
Josh
On Wed, Aug 27, 2014 at 11:47 PM, Josh Wasserstein
wrote:
> What prior does scikit-learn use for MultinomialNB? The document
Hi Karimkhan,
If I am understanding your question correctly, you are asking to classify
test data in a class that is not specified in your training set.
For instance if you have three classes of news article specified in your
training data (e.g. politics, sports, and food) and you try to classify
n documents clustering using a precomputed similarity metric between a pair
of documents.
Code so Far
Sim=np.zeros((n, n)) # create a numpy arrary
i=0
j=0
for i in range(0,n):
for j in range(i,n):
if i==j:
Sim[i][j]=1
else:
Sim[i][j]=simfunction(list_doc[i],list_doc[j]
This is due to the Laplace smoothening. If I understand correctly, you want the
classification to fail if there is a new feature value (e.g., a word that is
not in the vocabulary when you are doing text classification)?
You can set the alpha parameter to 0 (see
http://scikit-learn.org/stable/mo
I am using KNeighborsClassifier and trying to obtain probabilistic output.
But for many of the test sets I am getting equal probability for all class.
>>>X_train, X_test, y_train, y_test =
cross_validation.train_test_split(iris.data, iris.target, test_size=0.4,
random_state=0)
>>>clf = KNeighbors
I have trained my classifier using 20 domain datasets using MultinomialNB.
And it is working fine for these 20 domains.
Issue is, if I make query which contains text which does not belongs to any
of these 20 domain, even it gives classification result.
Is it possible that if query does not belon
Hello,
I have n documents and want to use precomputed similarity mertric between a
pair of documents for clustering.
I created a 2 dim numpy Array say X, containing similarity score for every
pair of documents.
Also
type(X) and X.shape gives the output as
(n, n)
Then I create a cluster object us
