Mahalanobis is always tricky, the covariance is between the features, not
the samples. This works:
OPTICS(metric='mahalanobis',metric_params={'VI':
np.linalg.inv(np.cov(test_array.T))}).fit(test_array)
Not sure why it wouldn't work when you pass V, as it suggests as an
alternative.
On Fri, May 3
Hello everyone,
I'm new to scikit learn. I see that many tutorial in scikit-learn follows
the work-flow along the lines of
1) tranform the data
2) split the data: train, test
3) instantiate the sklearn object and fit
4) predict and tune parameter
But, linear regression is done in least squares, s
