I wish I had known about tools for optimizing hyperparameters faster! Luckily, even though I wasn’t part of the top 50, I still qualified for the bootcamp. I ended up with about 90% accuracy by the end of the hackathon. This brought a little joy, my accuracy moved from 82 to 86%.Īfter a lot of trials and no improvement, I went back to manual tuning to see what I could gain. Frustrated, I decided to try RandomSearchCV. Even worse, the results from GridSearchCV weren’t better. I tried out GridSearchCV and took more than 3 hours to give me results from the range of values I provided. I knew about GridSearchCV and RandomSearchCV. I was already exhausted, imagine working 7 hours straight to improve a model. Excited, I started tuning other hyperparameters, but not all turned out so well. The accuracy moved grew to 82% by changing a parameter (this move is really important, as anyone who’d done a hackathon will attest!). I started tuning the model manually – got a bit better results. I needed to do something to improve my score on the leaderboard. After days of trying to improve and engineer features, the model’s accuracy seemed to oscillate around 80%. I had to predict if an employee should get a promotion or not. The dataset had information about certain employees. It was a pre-qualification hackathon for a bootcamp organised by Data Science Nigeria. I vividly remember a machine learning hackathon that I participated in two years ago, when I was at the beginning of my data science career.

Easy xgboost install windows