[MRG] Fix the match score scaling #802
Conversation
jeromedockes
changed the title
jeromedockes
changed the title
|
this is ready for review but I made it a draft so it is not merged by mistake, indeed #757 must be merged before to fix the example 04 |
jeromedockes
changed the title
| "joiner-2__match_score": [0.2, 0.9], | ||
| "joiner-3__match_score": [0.2, 0.9], | ||
| "joiner-1__match_score": [0.1, 0.9], | ||
| "joiner-2__match_score": [0.1, 0.9], |
There was a problem hiding this comment.
I can't comment on it but you need to update the description regarding the best match_score parameter here and in other places of the example.
"The grid searching gave us the best value of 0.5 for the parameter"
| # ``match_score``. Let's use this value in our regression: | ||
| # | ||
|
|
||
| print(f"Mean R2 score with pipeline is {grid.score(df, y):.2f}") |
There was a problem hiding this comment.
@Vincent-Maladiere having a closer look, this part is not informative: we are scoring on the training data. if we cross-validate the grid search correctly the score does not improve as much
There was a problem hiding this comment.
depending on the cv random state the grid search score can be the same or worse than the one without grid search
|
@jovan-stojanovic when you have time would you mind having a look at this one? (Note the example might still be improved in a later PR eg to highlight the gain of fuzzy_join vs a regular join, and we will think about other score rescaling strategies, but the goal of this PR is just to fix the scale so it starts at 0 and to remove the division by 0) |
closes #763 , #764
note example 04 will fail due to an issue with duplicate column names; this will have to wait until #757 is merged