Hi Roberto,
>From the docs:
X: array [n_samples, n_samples] or [n_samples, n_features]
Array of distances between samples, or a feature array.
The array is treated as a feature array unless the metric is
given as 'precomputed'.
In most cases, X is the
When using DBSCAN as in the examples:
db = DBSCAN(eps=0.3, min_samples=10).fit(X)
I'm not sure if I understand what X is.
Is X[i][j] supposed to be some sort of measure from node i to node j?
If so, does X need to be normalized, or will DBSCAN scale the values
accordingly?
Thank you,
-
> I think the encoders should all be able to deal with unknown labels.
> The thing about the extra single value is that you don't have a column
> to map it to.
> How would you use the extra value in LabelBinarizer or OneHotEncoder?
You're right, and this points to a difference between what PR #324
I think the encoders should all be able to deal with unknown labels.
The thing about the extra single value is that you don't have a column
to map it to.
How would you use the extra value in LabelBinarizer or OneHotEncoder?
For LabelEncoder I think it would make sense.
On 07/17/2014 12:59 AM, Ch
