Hello,
I have a text classification problem where I have about 50 classes and have 50
binary classifiers (1 per topic). The training set used to train each topic
classifier is different (some instances might overlap). Each instance consists
of a text snippet which is
transformed using tf-idf
stian
On Apr 28, 2015, at 10:40 PM,
nmura...@masonlive.gmu.edu<mailto:nmura...@masonlive.gmu.edu> wrote:
Hello,
I am very new to scikit-learn and am trying to run cross-validation on a data
frame consisting of text features, classification class. I am trying to perform
text data classific
Hello,
I am very new to scikit-learn and am trying to run cross-validation on a data
frame consisting of text features, classification class. I am trying to perform
text data classification. It is a 2-class classification problem where the
distribution between positive and negative instances i
This is in response to the thread on recommender system implementation in
scikit-learn. I would also like to know if any of the scikit-learn contributors
are willing to mentor a project which implements basic recommender system
algorithms - collaborative filtering (user-based/item-based/matrix
Firstly you need to preprocess your data a good tool for that is PANDAS. That
is 60% of any machine learning task as you will see. What is the goal you are
trying to achieve?
If you don't have labelled data, again I only glanced at your post.
Unsupervised learning is a good way to go in which c
I agree that sparse matrices need to be supported as one of the main
properties inherent to the user/item rating matrix in recommender systems is
its sparsity. This sparsity is what has given rise to such a large scale of
research in the field. Hence this property would have to be taken advanta
I am running python 2.7.3, using enthought canopy and am having issues with
fetching the twenty news groups dataset. It says empty file when I try using
the code provided in the example on the following page:
http://scikit-learn.org/stable/datasets/twenty_newsgroups.html
The first two lines of