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I think that a major missing gap in parsnip is explicit support for ordinal models, by which I mean **models where the response variable is an ordered factor**.
My proposal here is a follow up to […
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I’ve also noticed that the evaluation of the regression model includes classification metrics such as accuracy, precision, recall, F1 score, and confusion matrix. These metrics are specifically desig…
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We could use tidymodels + parsnip for allowing all kinds of models for IPCW estimation.
- only logistic regression?
- also "classification" models?
- Wrapper for fitting, saving, predicting
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### Feature description
[machine_learning/xgboost_classifier.py](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/xgboost_classifier.py) and [machine_learning/xgboost_regressor.…
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Experiment and Implement classification models -
1. Decision Tree
2. Random Forest
3. Logistic Regression
4. Naive Bayes models.
Evaluate and compare models on metrics (accuracy, precision, …
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**Build Scans:**
- [elasticsearch-intake #12737 / part4](https://gradle-enterprise.elastic.co/s/empeykqumytbo)
- [elasticsearch-pull-request #40075 / part-4](https://gradle-enterprise.elastic.co/s/32y…
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According to [Intel site LightGBM should already have OneDAL support](https://www.intel.com/content/www/us/en/developer/articles/technical/improve-performance-xgboost-lightgbm-inference.html).
But …
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**Describe the bug**
I was running LazyClassifier for a classification problem on my data, however, it returned the result of a regression problem as the output metrics are all regression metrics. I …
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Currently, the evaluate_model function focuses primarily on accuracy and F1-score for classification models, and MSE and R² for regression models. We could enhance this by including additional evaluat…
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Developed a machine learning model to detect fraudulent credit card transactions with 93% accuracy on a dataset of 284,807 transactions. Preprocessed and normalized the data while handling class imbal…