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Implement the `LogisticRegression` Class for binary classification problems.
`LogisticRegression` class should have these methods:
- `.fit(X,y)` :- it takes in features(X) and targets(y) and train…
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Hi!
I want to use mljar for binary classification (category1+category2).
The parameters I am passing to AutoML are the following:
```
automl = AutoML(results_path=str(model_directory),
…
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### Summary
Both segmentation and object detection require that the background be included and there is currently a note on these args: `num_classes: Number of prediction classes (including the backg…
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#### Software execution information
MLE-agent version: 0.3.1
System OS version: ubuntu 20.04.6
#### Problem description
in my scene, i use ollama with llama3.1-8b model. Following the exa…
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### Environment Details
Please indicate the following details about the environment in which you found the bug:
* SDGym version: sdgym-0.7.0
* Python version: 3.9 (probably Any)
* Operating Sy…
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- **Operating System**: linux
- **Python Version**: 3.10.14
- **XGBoost Version**: 2.1.0
I am experiencing a segmentation fault with XGBoost 2.1.0 when trying to access feature importances in a m…
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After messing around, I could not use the package on my case. So I tried to run locally your your binary classification example from here: https://deeptables.readthedocs.io/en/latest/examples.html#bin…
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I already augmented the dataset, trained the (.h5) model but i get a bunch of errors inside my IDE.
`**### **0%| | 0/1 [00:00
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In binary classification, there are situations where the data are imbalanced and only false positives or only false negatives are costly, we would want to tune the model on simply Precision or Recall.…
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It would be nice to support the [Benchopt](https://github.com/benchopt/benchopt) problem suite, which is also available in Python:
- [ ] Ordinary Least Squares
- [ ] Non-Negative Least Squares
- …