- Year: 2022
- Organisation: TensorFlow
- Project Title: Develop Kaggle examples for TensorFlow Decision Forests
- Project Description: Today, many Kaggle competitions are dominated by libraries such as LightGBM and XGBoost, which provide high-performance results on a variety of datasets. TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. In this project, I have introduced a series of beginner and intermediate examples that demonstrates the use of TensorFlow Decision Forests on various datasets.
- Mentor: Miri B. (Hyman) Raskasky
| Description | Status |
|---|---|
Kaggle example - Intermediate classification  |
Completed |
| Kaggle example - Beginner classification |
Completed |
Kaggle example - Beginner regression  |
Completed |
| Kaggle example - Intermediate regression |
Completed |
Contributions apart from my project:
| Link | Description |
|---|---|
| PR #134 | Documentation Changes |
| Notebook | [Non Kaggle example] - Classification with TF-DF |
My Google Summer of Code Experience was awesome and a large part of this great experience was the good mentoring of Miri B. (Hyman) Raskasky.
I thank her for the constant guidance, code reviews, timely feedback, help and most importantly, for her dedicated advice and encouragement throughout GSoC. I would love to contribute more to TensorFlow.
