docs: correct readme example

[KameniAlexNea]

Target attribute, not exist in first code example

[aerdem4]

Why did you change kfold params?

[KameniAlexNea]

We can't set shuffle to False and random_state set to 0 as in this capture of my notebook.

[aerdem4]

Maybe it changed with a new sklearn version. Anyway, you can set the random_state to None but shuffle should stay as False since it is doing a lazy time split.

[KameniAlexNea]

Yep, it seems like the API has changed.

[stephanecollot]

Maybe good to add a comment and make the example a bit more general:

sample_df.sort_values("AvSigVersion", inplace=True)
->
sample_df = sample_df.sample(frac=1) # Shuffling rows before CV

and
cv = KFold(n_splits=4, shuffle=False) # No shuffling to keep the same folds for each feature

[aerdem4]

I thought anyone could search for the competition name and get the data but we can also share the link to the competition on readme? We can also comment that AvSigVersion is a proxy for time because the data had no time column.

And having a time split validation is maybe not the best readme example you want to show.

Why?

[stephanecollot]

not the most common use case.
Ok, now that I read more the readme, you are right that section is called "Example on Kaggle's Microsoft Malware Prediction Competition"
So

sample_df.sort_values("AvSigVersion", inplace=True)  # Sort by time for time split validation

cv = KFold(n_splits=4, shuffle=False)  # Don't shuffle to keep the time split split validation

Maybe good to add a generic example based on sklearn data like Iris.

[aerdem4]

These comments should make it easier to understand 👍

LOFO is usually more useful for non-random split problems but such example could also be nice.

[aerdem4]

@KameniAlexNea can you please add these 2 comments above that @stephanecollot shared since you already update the readme? Then I can merge it.

[KameniAlexNea]

I just make a push now