Hi Tom
This was also the first thing that came to my mind, but I thought sincr
your_df is X_train+X_test
it may complain that values do not match with the given indices.
Thanks,
Ruchika
On Thu, Jul 20, 2017 at 12:19 PM, Tom Augspurger wrote:
> Something like
>
> your_df['prediction'] = pd
Something like
your_df['prediction'] = pd.Series(clf.predict(X_test),
index=X_test.index)
should handle all the alignment.
On Thu, Jul 20, 2017 at 11:04 AM, Ruchika Nayyar
wrote:
> The original dataset contains both trainng/testing, I have predictions
> only on testing dataset. If I do wha
The original dataset contains both trainng/testing, I have predictions only
on testing dataset. If I do what you suggest
will it preserve indexing?
Thanks,
Ruchika
On Thu, Jul 20, 2017 at 11:37 AM, Julio Antonio Soto de Vicente <
ju...@esbet.es> wrote:
> Hi Ruchika,
>
> The predictions outputte
Hi Ruchika,
The predictions outputted by all sklearn models are just 1-d Numpy arrays, so
it should be trivial to add it to any existing DataFrame:
your_df["prediction"] = clf.predict(X_test)
--
Julio
> El 20 jul 2017, a las 17:23, Ruchika Nayyar escribió:
>
> Hi Scikit-learn Users,
>
> I
Hi Scikit-learn Users,
I am analyzing some proxy logs to use Machine learning to classify the
events recorded as either "OBSERVED" or "BLOCKED". This is a little snippet
of my code:
The input file is a csv with tokenized string fields.
**
# load the file
M = pd.read_csv("output100k.cs
