X needs to be a matrix of shape (n_samples, n_features), not a vector. You
need to reshape it into the matrix by doing
X_train = X_train.reshape( (len(X_train), 1) )
On Mon, Feb 16, 2015 at 4:01 PM, shalu jhanwar <shalu.jhanwa...@gmail.com>
wrote:
> Hi Scikit fans,
>
> I am facing following error while performing classification with *single
> feature* only:
>
> reg = linear_model.LogisticRegression()
> *scores.append(reg.fit(X_train, y_train).score(X_test, y_test))*
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File
> "/software/so/el6.3/PythonPackages-2.7.6/lib/python2.7/site-packages/sklearn/svm/base.py",
> line 676, in fit
> (X.shape[0], y.shape[0]))
> *ValueError: X and y have incompatible shapes.*
> X has 1 samples, but y has 1692.
>
> My variables look like:
>
> >>>X_train
> array([ 1., 1., 1., ..., 1., 1., 1.])
> >>>X_train.shape
> (1692,)
> >>>y_train
> array([ 0., 0., 1., ..., 0., 1., 1.])
> >>>y_train.shape
> y_train.shape
> (1692,)
>
>
> Can someone help me removing this error?
>
> Thanks!
> Shalu
>
>
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