Hi, please repeat your experiments several times because both Random Forest
Embedding and your resampling are not deterministic algorithm. their
results may varys a lot
please also check the attributes "n_support_" and "support_vectors_" in the
object, they're highly related to time complexity
2014-03-17 13:59 GMT+08:00 Caleb <cloverev...@yahoo.com>:
> Hi Olivier,
>
> Thanks for your advices. Maybe I should rephrase my question. The basic
> situation is shown below.
>
> T1
> |--------> LinearSVC (longer training time, about 30s)
> data -- T2
> |--------> LinearSVC (significantly shorter training time, about
> 3s)
>
> I transform my data using different transformation T1 and T2 and then feed
> it into the LInearSVC. What I found is that the classifier is trained
> significantly faster with the transformed data using T2.
>
> Since both transformed data has the same number of instances, we are
> looking at factors other than number of instances that affect the training
> time. So my question is basically what are the properties of a dataset that
> make the LinearSVC training time shorter?
>
> In my case, T1 is RandomForestEmbedding trained with random data and T2 is
> RandomForestEmbedding trained with the original data. Since T1(data) and
> T2(data) are both sparse matrix of similar size, I am wondering what are
> the other factors that make the training time significantly shorter?
>
> ---
> Caleb
>
>
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