data dredging snooping hyperparameters bayesian

Model hyperparameter – Hyperparameters are those values you can tune manually from the model itself, like the learning rate, number of estimators, type of regularization, etc.Model parameter – A model parameter is that which your model learns from the data, like features, relationships, etc., which you can’t manually tune (not feature engineering).Now, I’d like to discuss some terms that I’ll be using in the article: Set up your full model pipeline before training.Use early stopping rounds with large epochs to prevent overfitting.Separate your data into training, validation and test sets.You can get this with smaller models, fewer iterations, default parameters, or a manually tuned model. Now, I know that there are good hyperparameter tuning tools I could’ve used, and I’m excited to share them with you.īefore you start hypertuning, make sure these things are done: 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.

Data dredging snooping hyperparameters bayesian