Comments (5)
Hey,
this might have to do with the implicit assumptions about the data types and thus the chosen evaluation metric.
e.g. numeric data with only a few categories have been treated as categoric but now they are treated as numeric unless you change their data type.
Can you maybe share some more information about the columns that changed their scores? e.g. data types, unique values, distributions, ppscore before?
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Hi Florian Wetschoreck,
Unfortunatly, I am not able to share much of the data here. I have tried with a different dataset now and all values of the ppscore are 0 (which I doubt it)
As mentioned I had to upgrade te ppscore because it was given me an error [AttributeError: module 'ppscore' has no attribute 'predictors' ]. When doing so I lost all data on the previous scores but I have multiple ones with more than 0 and now just 2. I am sure this has to do with something of the dtypes (it is worth mentioning that data and code is the same, the only change was the upgrade of version so it is kind of strange in that sense).
I can share the results of some coulmns and the data types below. As mentioned I just tried a diferent dataset and all ppscores are 0.
This library was really usefull to me so if there is anything I can help let me know.
sabi_sum_ingresos_ult1 float64
sabi_sum_result_ult1 float64
sabi_sum_empleados_ult1 int64
sabi_bas_condicion object
sabi_bas_falcon_crs_actual int64
sabi_bas_falcon_prob_incump float64
sabi_bas_falcon_lim_credito int64
sabi_bas_more_punt_corriente object
sabi_bas_more_prob_incump float64
sabi_bas_more_credit_limit int64
sabi_bas_wvb_global_score object
sabi_bas_puntuacion_crif int64
sabi_bas_crif_min int64
sabi_bas_vadis_p2bb float64
target int64
from ppscore.
Hi Jose,
sorry to hear that the new results are not so useful to you anymore.
Currently, you have the following options:
- adjust the dtypes of your columns to string (categoric)
- downgrade ppscore to a lower version (probably without being able to use .predictors any more but you can just manually calculate this yourself when looping over the columns)
Please let me know if this worked for you,
Florian
from ppscore.
I simply went back to 0.0.4 and it worked without changing anything
from ppscore.
Happy to hear that :)
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Related Issues (20)
- Data preprocessing and information leakage HOT 14
- [SUGGEST] Release a verson supported GPU HOT 2
- ppscore when model_score>baseline_score HOT 3
- There should be an option to override the attribute type like PyCaret HOT 4
- Scikit-learn dependency < 1.0.0 HOT 14
- [Suggestion]: Plot the Decision Tree for pps.score HOT 3
- Readme / docs unclear about using ppscore on time series data HOT 3
- pytests failing with pandas==1.4.0 HOT 1
- Thought on a possible enhancement of the PPS HOT 2
- What does PPS score? HOT 4
- Add support to release Linux aarch64 wheels HOT 4
- Cannot install ppscore HOT 1
- Your package isn't compatible with scikit-learn 1.0.1 HOT 2
- How to report PPS HOT 1
- Question About Data Order HOT 12
- y predicted values given x HOT 3
- Performance HOT 3
- differnt baseline scores for the same y HOT 1
- How to deal with heavy imbalanced data? For example, when the target is 99 "negative" to 1 "positive"
- pandas >2 support HOT 1
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