Diving Deeper into Machine Learning: Exploring Updates, Analysis, and Practical Insights on Our Blog
What is Feature Engineering? Feature engineering is the process of improving a model’s accuracy by using domain knowledge to select and transform raw data’s most relevant variables into features…
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In part one of this two-part series, we explored basic models and data enrichments for our hit song classifier. In this article, we will try to push our model a…
It comes as no surprise that the music industry is tough. When you decide to produce an artist or invest in a marketing campaign for a song there are many…
It’s important to understand that none of the following evaluation metrics for classification are an absolute measure of your machine learning model’s accuracy. However, when measured in tandem with sufficient…
Feature selection algorithms are increasingly growing in significance. In this article, we will cover (and compare) two popular feature selection methodologies – Filter and Wrapper.
The whole idea behind interpretable and explainable ML is to avoid the black box effect.
The whole idea behind interpretable and explainable ML is to avoid the black box effect.
While there is some room for error while integrating models into production environments, there is also a very good probability that these issues will eventually lead to disaster. And that’s exactly why we have created this pre-model deployment checklist.
Better features, better data
Demystifying the old battle between transparent, explainable models and more accurate, complex models.
