{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/f279af6c56854f3fad1a082d20f252af\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/f279af6c56854f3fad1a082d20f252af-2eb7dee02f2146db.gif","duration":555.174,"title":"Model Selection","description":"In this video, I walk you through my process for selecting and evaluating a model using XGBoost classification. I discuss the importance of AUC scores, especially in the context of class imbalance, and share my findings from tuning parameters and feature selection. I found that using all features improved our model's performance significantly, achieving a test score of 0.9815. Please review the details and let me know your thoughts on the parameter tuning strategies I employed."}