wangyuhsin / feature-importance-and-selection

Feature importance refers to a measure of how important each feature/variable is in a dataset to the target variable or the model performance. It can be used to understand the relationships between variables and can also be used for feature selection to optimize the performance of machine learning models.

Hi 👋, I'm Yu Hsin

✨ A passionate Data Scientist

🎓 Data Science Graduate @ University of San Francisco

💼 Currently working as a Data Science intern @ Salk Institute

🤖 Machine Learning: Deep Learning, Regression, Decision Tree, Clustering, Gradient Boosting, XGBoost, Random Forest

⚒ MLOps: MLflow, Weights & Biases, DVC, Great Expectations, Metaflow, Airflow, Evidently, Streamlit

📫 How to reach me: You can message me on LinkedIn or by Email

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