Another solution is to use SVR(kernel="poly", degree=2).
Mathieu
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The following example explains how to do it using the numpy.vander function:
http://scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html
Mathieu
On Wed, Aug 8, 2012 at 11:27 AM, Zach Bastick wrote:
> How can you increase the degree of the polynomial for multivari
How can you increase the degree of the polynomial for multivariate
LinearRegression?
Numpy.polyfit has a "deg" parameter, allowing you to choose the degree
of the fitting polynomial, but doesn't work with multivariate data:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.polyfit.html
F
I am trying
http://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction
(6.2.2.3. Common Vectorizer usage).
I did
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
print vectorizer
corpus = [
'This is the first document.',
according to
http://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction
(6.2.2.3. Common Vectorizer usage),
I did:
>>> from sklearn.feature_extraction.text import CountVectorizer
>>> vectorizer = CountVectorizer()
but I get
>>> vectorizer
CountVectorizer(analyzer=Wor
I tried to import DictVectorizer from sklearn.feature_extraction and I got an
error: why?
>>> from sklearn.feature_extraction import DictVectorizer
Traceback (most recent call last):
File "", line 1, in
from sklearn.feature_extraction import DictVectorizer
ImportError: cannot import name
thanks
the branch just got merged :)
Alex
On Mon, Aug 6, 2012 at 2:33 PM, federico vaggi wrote:
> Worked - here is the build log.
>
>
>
> On Mon, Aug 6, 2012 at 1:34 PM, Alexandre Gramfort
> wrote:
>>
>> hi federico,
>>
>> have you followed the discussion on the PR:
>>
>> https://github.com/sc
