I am using scikit learn to calculate the basic chi-square statistics(sklearn.feature_selection.chi2(X, y)):
def chi_square(feat,target):
""" """
from sklearn.feature_selection import chi2
ch,pval = chi2(feat,target)
return ch,pval
chisq,p = chi_square(feat_mat,target_sc)
print(chisq)
print("**********************")
print(p)
I have 1500 samples,45 features,4 classes. The input is a feature matrix with 1500x45 and a target array with 1500 components. The feature matrix is not sparse. When I run the program and I print the arrray "chisq" with 45 components, I can see that the component 13 has a negative value and p = 1. How is it possible? Or what does it mean or what is the big mistake that I am doing?
I am attaching the printouts of chisq and p:
[ 9.17099260e-01 3.77439701e+00 5.35004211e+01 2.17843312e+03
4.27047184e+04 2.23204883e+01 6.49985540e-01 2.02132664e-01
1.57324454e-03 2.16322638e-01 1.85592258e+00 5.70455805e+00
1.34911126e-02 -1.71834753e+01 1.05112366e+00 3.07383691e-01
5.55694752e-02 7.52801686e-01 9.74807972e-01 9.30619466e-02
4.52669897e-02 1.08348058e-01 9.88146259e-03 2.26292358e-01
5.08579194e-02 4.46232554e-02 1.22740419e-02 6.84545170e-02
6.71339545e-03 1.33252061e-02 1.69296016e-02 3.81318236e-02
4.74945604e-02 1.59313146e-01 9.73037448e-03 9.95771327e-03
6.93777954e-02 3.87738690e-02 1.53693158e-01 9.24603716e-04
1.22473138e-01 2.73347277e-01 1.69060817e-02 1.10868365e-02
8.62029628e+00]
**********************
[ 8.21299526e-01 2.86878266e-01 1.43400668e-11 0.00000000e+00
0.00000000e+00 5.59436980e-05 8.84899894e-01 9.77244281e-01
9.99983411e-01 9.74912223e-01 6.02841813e-01 1.26903019e-01
9.99584918e-01 1.00000000e+00 7.88884155e-01 9.58633878e-01
9.96573548e-01 8.60719653e-01 8.07347364e-01 9.92656816e-01
9.97473024e-01 9.90817144e-01 9.99739526e-01 9.73237195e-01
9.96995722e-01 9.97526259e-01 9.99639669e-01 9.95333185e-01
9.99853998e-01 9.99592531e-01 9.99417113e-01 9.98042114e-01
9.97286030e-01 9.83873717e-01 9.99745466e-01 9.99736512e-01
9.95239765e-01 9.97992843e-01 9.84693908e-01 9.99992525e-01
9.89010468e-01 9.64960636e-01 9.99418323e-01 9.99690553e-01
3.47893682e-02]
