Comments (1)
Hi Maddy,
thank you for pointing this out. The reason for this is that there are some parts of the PPS which are not deterministic but instead rely on random samples.
Currently, the default for sampling is that a new random_seed is drawn for each calculation and thus you do not see consistent results.
We had an internal discussion and think that we prefer to set the random_seed per default.
So, in the next version of PPS the results will be the same.
Best,
Florian
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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
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