Hi,
As it has been indicated by other members, methods such as
``LocalOutlierFactor`` do not expose a ``predict`` method by design.
However, if you nevertheless would still like to keep experimenting in the
direction of attempting to make predictions on "unseen" data, you could
simply create a s
What are you trying to achieve with this code?
If you label everything as 1, the highest accuracy will be obtained if
everything is labeled as 1.
So even if the interface was implemented, the result would not be helpful.
On 10/06/2017 12:53 AM, Lifan Xu wrote:
Hi,
I was trying to train a
Hi,
As Joel said LOF is not designed to be applied on unseen data. Therefore
there is no public predict.
Albert
On Sun 8 Oct 2017 at 06:17, Joel Nothman wrote:
> actually I'm probably wrong there, but you may not be able to use accuracy
> ___
> sciki
actually I'm probably wrong there, but you may not be able to use accuracy
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I don't think LOF is designed to apply to unseen data.
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Hi,
I was trying to train a model for anomaly detection. I only have
the normal data which are all labeled as 1. Here is my code:
clf =
sklearn.model_selection.GridSearchCV(sklearn.neighbors.LocalOutlierFactor(),
parameters,
scoring="acc
