Boosting Model Accuracy: Techniques I Learned During My Machine Learning Thesis at Spotify (+Code…
<p>My goal was to understand what made users satisfied with their music experience. To do so, I built a LightGBM classifier whose output was a binary response:<br />
<em>y = 1 → the user is seemingly satisfied<br />
y = 0 → not so much</em></p>
<p>Predicting human satisfaction is a challenge because humans are by definition unsatisfied. Even a machine isn’t so fit to decipher the mysteries of the human psyche. So naturally my model was as confused as one can be.</p>
<h2>From Human Predictor to Fortune Teller</h2>
<p>My accuracy score was around 0.5, which is the worst possible outcome you can get on a classifier. It means the algorithm has a 50% chance of predicting yes or no, and that’s as random as a human guess.</p>
<p>So I spent 2 months trying and combining different techniques to improve the prediction of my model. In the end, I was finally able to improve my ROC score from 0.5 to 0.73, which was a big success!</p>
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