Thanks, I removed the uneccessary fit method.
Regarding normalization, aren't the features automatically normalized
with the l2 norm when using tfid?
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(trainingTexts)
Just in case, I added the following but get the same results anyway:
from sklearn import preprocessing
normalizer = preprocessing.Normalizer().fit(X)
X = normalizer.transform(X)
However, removing the parameters from
vectorizer = TfidfVectorizer()
now gives me better (but still bad) output for LinearRegression:
Accuracy: 0.40 (+/- 0.05)
I've updated these small changes to the zip that I uploaded here:
https://dl.dropbox.com/u/74279156/regression.zip
No idea what to try next...
Zach
On 12/08/2012 02:52, Andreas Mueller wrote:
Just a small comments:
You don't need to `fit` the models before using ``cross_valid_score``.
They are refit for each split anew.
Btw, have you tried normalizing your responses?
Cheers,
Andy
On 08/12/2012 07:02 AM, Zach Bastick wrote:
Sorry about that, the RTF reader is from the Pyth library:
http://pypi.python.org/pypi/pyth/
I think that's all that's needed.
Thanks for taking a look!
Zach
On 11/08/2012 22:55, Robert Layton wrote:
On 12 August 2012 15:35, Zach Bastick <[email protected]
<mailto:[email protected]>> wrote:
I have tried various machine learning algorithms from scikit
learn but
can't find a good prediction model.
The features I'm using are the tf-idf of set of text documents,
correlated with human ratings assigned to each document. I'm
thinking
that I must be doing something wrong as the scores can't be that bad
(not to mention negative?)
If someone could have a look at it, I'd really appreciate it. I
didn't
upload to a github gist because they won't let me upload the dataset
directory. So I've uploaded my really short code (regression.py)
AND the
original data set (/texts) here (625K):
https://dl.dropbox.com/u/74279156/regression.zip
This is my output:
C:\python code\program>python regression.py
loading texts...
n_samples: 53, n_features: 6284
LinearRegresson
[ 0.34662496 0.23446674 0.30332109 0.3163838 0.01607913]
Accuracy: 0.24 (+/- 0.06)
SVR linear
[-0.05521329 -1.61280714 -0.67428098 -0.8805647 -2.20730703]
Accuracy: -1.09 (+/- 0.37)
SVR poly 4 degrees
[-0.18814233 -1.78480475 -0.88158686 -1.05944432 -2.40284073]
Accuracy: -1.26 (+/- 0.38)
SVR sigmoid
[-0.18814233 -1.78480475 -0.88158686 -1.05944432 -2.40284073]
Accuracy: -1.26 (+/- 0.38)
Please tell me what's wrong.. I'm dying to know how to get
scikit-lean
to predict based on this dataset.
Thanks
Zach
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