Tag: Accuracy

Boosting Model Accuracy: Techniques I Learned During My Machine Learning Thesis at Spotify (+Code…

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: y = 1 → the user is seemingly satisfied y = 0 → not so much Predicting human satisfaction is a challenge because humans are by d...

Boosting Model Accuracy Techniques I Learned During My Machine Learning Thesis at Spotify (+Code…

In 2021, I spent 8 months building a predictive model to measure user satisfaction as part of my Thesis at Spotify. Image by Author 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 respo...

Boosting Model Accuracy: Techniques I Learned During My Machine Learning Thesis at Spotify (+Code Snippets)

This article is one of a two-part piece documenting my learnings from my Machine Learning Thesis at Spotify. Be sure to also check out the second article on how I implemented Feature Importance in this research. Feature Importance Analysis with SHAP I Learned at Spotify (with the Help of the...

How to Avoid Being Fooled by Model Accuracy

The metrics used for gauging performance of classification models are fairly straightforward, at least from a mathematical standpoint. Nevertheless, I have observed that many modellers and data scientists encounter difficulty articulating these metrics, and even apply them incorrectly. This is an ea...

Evaluating Model Performance in Medical Diagnosis: Understanding Confusion Matrix Metrics

In the realm of medical diagnosis, the use of machine learning and artificial intelligence has become increasingly prevalent. These technologies have the potential to aid healthcare professionals in making accurate and timely diagnoses, ultimately improving patient outcomes. However, the effectivene...

Types of Errors in Measurement. Accuracy and Precision.

Types of Errors in Measurement There are two types of measurement errors… 1. random errors (human errors) 2. systematic errors (instrumentation errors) Random Errors Random error = measurement errors that have an equal probability of being too high or too low, too ...

How to Avoid Being Fooled by Model Accuracy

The metrics used for gauging performance of classification models are fairly straightforward, at least from a mathematical standpoint. Nevertheless, I have observed that many modellers and data scientists encounter difficulty articulating these metrics, and even apply them incorrectly. This is an ea...