This project is a reboot of an earlier project predicting bicycle ride share riders destinations.

Looking at hyperparameter tuning results

Summary Here is an early draft of a post, trying to extract some of the insights from the project here. There is a lot to write about and I want to just start getting it out. Quick outline The logloss upper bound Does the “k area” metric help? training balancing Is it possible to calculate the Bayesian error rate here? And logloss seems to be very sensitive. (can look at correlations , not super high) So what metric should be used ?...

Let’s summarize I want to just summarize some learnings from some of my recent notebooks yea. I have picked up my bike share data learning project from earlier, to try to redo it after having gathered more experience. I want to just jot down some ad hoc thoughts here. This is mainly around navigating XGBoost. There are two XGBoost APIs With the sklearn API you can import xgboost as xgb from xgboost import XGBClassifier import numpy as np from sklearn....

Not sure yet. There are a fitness trackers out there, but I am curious if my chest band can help. I took a quick look at one of my recordings on the Wahoo app, but I don’t see anything more granular than just beats per minute. The app indeed has pushed to Apple Health, but I only see the bpm data and no HRV data. Didnt finish this but I tried parsing the raw fit file Making a note for later I suppose....