Re: [Scikit-learn-general] Strings as features

In which version of sklearn, is the above mention 'make_pipeline' and 'make_union' defined?? When I read through some example, the idea of using FeatureUnion and Pipelined are easy, I guess. Former chains the features obtained from each individual estimators given as the input were as the latter u

Re: [Scikit-learn-general] PCA inverse transform

Kyle is facing the same question for his incremental pca pr https://github.com/scikit-learn/scikit-learn/pull/3285 On Monday, June 30, 2014, Michael Eickenberg wrote: > Hi Sean, > > this has been mentioned in an issue > https://github.com/scikit-learn/scikit-learn/pull/3107 along with the > chan

Re: [Scikit-learn-general] PCA inverse transform

Hi Sean, this has been mentioned in an issue https://github.com/scikit-learn/scikit-learn/pull/3107 along with the changes necessary to invert the whitening properly (if you look at files changed). While we are at it, Alex Gramfort asked me to ask whether anybody sees good reasons *not* to invert

Re: [Scikit-learn-general] Retrieve the coefficients of fitted polynomial using LASSO

The model selection properties of the lasso are such that it will select a certain number of variables that suit it best. If you like, you can run a lasso and then select the degree of the polynomial of maximum degree active and call it the polynomial order of your problem. I maintain that the set

[Scikit-learn-general] PCA inverse transform

Hi Why doesn't PCA and Probabilistic PCA calculate the inverse transform properly when whitening is enabled? AFAIK all that is required is to (in addition) multiply by explained_variance? sean On Mon, Jun 30, 2014 at 5:28 AM, < scikit-learn-general-requ...@lists.sourceforge.net> wrote: > Send

[Scikit-learn-general] Retrieve the coefficients of fitted polynomial using LASSO

Note2: In summary, I want the coefficients a_i without having to pre-define neither the degree of the polynomial to fit (n) nor the amount of regularization to apply (alpha), and always preferring the simpler model (less coefficients). -fernando On Sun, Jun 29, 2014 at 6:52 PM, Fernando Paolo

Re: [Scikit-learn-general] Retrieve the coefficients of fitted polynomial using LASSO

Michael and Mathieu, thanks for your answers! Perhaps I should explain better my problem, so you may have a better suggestion on how to approach it. I have several datasets of the form f = y(x), and I need to fit to these data a 'linear', 'quadratic' or 'cubic' polynomial. So I want to (i) *automa

[Scikit-learn-general] Difference between sklearn.feature_selection.chi2 and scipy.stats.chi2_contingency

Hi, Suppose I wanted to test the independence of two boolean variables using Chi-Square: >>> X = numpy.vstack(([[0,0]] * 18, [[0,1]] * 7, [[1,0]] * 42, [[1,1]] * 33)) >>> X.shape (100, 2) I'd like to understand the difference between doing: >>> sklearn.feature_selection.chi2(X[:,[0]], X[:,1]) (

Re: [Scikit-learn-general] Retrieve the coefficients of fitted polynomial using LASSO

Hi Fernando, On Sun, Jun 29, 2014 at 1:53 PM, Fernando Paolo wrote: > Hello, > > I must be missing something obvious because I can't find the "actual" > coefficients of the polynomial fitted using LassoCV. That is, for a 3rd > degree polynomial > > p = a0 + a1 * x + a2 * x^2 + a3 * x^3 > > I w

Re: [Scikit-learn-general] Retrieve the coefficients of fitted polynomial using LASSO

Running your code and then plotting plt.plot(x, lasso_predict) plt.plot(x, y) shows a pretty good correspondence (r^2 = .97)! I think the problem is that Xpoly does not contain raw polynomials as you use for prediction. If you do p_lasso = a[0] + a[1] * Xpoly[:, 1] + a[2] * Xpoly[:, 2] + a[3]