Blog Post #2: Balancing Learning Rates

Hello everyone, welcome back to my blog! For this week, I’ve been doing a lot more research and reading on machine learning. While I’ve essentially been doing the same tasks as last week, I feel like I’ve learned a lot more about the different model algorithms and selectors available for me to use for my project.

Among the many techniques I’ve learned about, an interesting one that I’ve learned about is called boosting. When training multiple machine learning models in sequence where each improves upon their predecessors, boosting makes these models improve upon previous mistakes specifically. The thing is, how fast these models are made depends on the learning rate: higher rates mean less time is taken, yet the model may make less specific predictions. While I’ve been doing a lot of trial and error through practice problems, I feel it’s about time I outline my specific project with more detail – I feel that I’ll be more efficient in studying the techniques I need given the knowledge I’ve accumulated over these past few weeks. So, I plan to devote next week to doing exactly that.

Until next week,
Elysse Ahmad Yusri