Dear SciKitters,
when running a PCA on a rather small dataset, I end up in the situation
that the first principal component is predominant.
My dataset contains 694 samples with 177 features each.
Here comes my code
"
X = dataDescrs_array
y = dataActs_array
target_names = ['inactive','active']
pca = PCA(n_components=2)
X_r = pca.fit(X).transform(X)
print 'explained variance ratio (first two components):', \
pca.explained_variance_ratio
"
=>
explained variance ratio (first two components): [ 1.00000000e+00
1.22493763e-32]
=> output of X.shape, y.shape, target_names:
X.shape (694, 177)
y.shape (694,)
target_names ['inactive', 'active']
When using different datasets (more samples, again 177 features per
sample), the PCA result is still the same.
I fear that I mixed up my syntax...
Cheers & Thanks,
Paul
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