我是否保留具有数字类别的要素或创建虚拟变量？

Question

I'm working with a dataset that has a combination of numeric features and features that are categories but encoded with integers. For example if it were a horse race,

horse_id   race_date    track_no        race_number    barrier_number  won_race  
1          2016-10-01   100             1              4               1
2          2016-10-01   100             1              3               0
1          2016-10-15   200             3              5               0
...

So, if I'm creating a model of a horse's probability of winning a race, and using the features like race_number (there can be several races on the same track on the same day so that should have an effect on track conditions) and barrier_number (a horse might prefer to be in the inside barriers or outside ones etc.), should i leave those features as it is or create dummy variables indicating 1 (presence) and 0 (absence) of the variable on each row?

This is a trivial example but these columns could have a large number of possible values and creating dummy variables will increase the dimension of the features a lot. Is that a tradeoff one has to make, or just leaving a single column do?

Edit: Also, if I leave the columns as it is and covert it into a caregory dtype in pandas, is that a good practice? Will existing ML libraries like Scikit-learn handle that correctly?

Answer 1

For the features described ( race_number , barrier_number ) I believe it's perfectly fine to leave as is. However, for the example above, i would encode the track_no feature.

This is because there is no relation between the individual track_no values.

I would turn the above example to:

horse_id   race_date    track_100      track_200        race_number    barrier_number  won_race  
1          2016-10-01   1              0                1              4               1
2          2016-10-01   1              0                1              3               0
1          2016-10-15   0              1                3              5               0

I hope that helps!