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Ensemble learning technique that builds models sequentially, with each model correcting errors made by the previous one.
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The use of Gradient Boosting Machines algorithms ( XGBoost) or Neural Networks can enhance the performance of the model .
Please assign me ,to work on this issue.
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This is the 2nd most popular model on Kaggle.
Thanks.
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### Is there an existing issue for this?
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### Feature Description
It provides classification, regression, and clustering algorithms such as the [SVM] algori…
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### pycaret version checks
- [X] I have checked that this issue has not already been reported [here](https://github.com/pycaret/pycaret/issues).
- [X] I have confirmed this bug exists on the [latest…
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#### Description
To enhance the performance of the flight delay prediction model, we should explore the use of more advanced machine learning models and perform hyperparameter tuning. The following s…
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I am currently exploring the application of XGBoost in medical diagnostics, specifically in the initial screening diagnosis of glaucoma using structured real medical data. I came across this project a…
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### Deep Learning Simplified Repository (Proposing new issue)
:red_circle: **Project Title** : Airlines Delay Prediction
:red_circle: **Aim** : Airlines Delay Prediction is a machine learning pro…
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Building a GBM model with a 33000 sources and 40 attributes training frame and these parameters:
{quote}
\{"model_id":"33k_GBM_MODEL","training_frame":"33K_frame","nfolds":0,"response_column":"CLASSI…
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# How to Implement a Gradient Boosting Machine that Works with Any Loss Function | Random Realizations
Summarize Freedman’s seminal GBM paper and implement the generic gradient boosting algorithm to …