exponentially-scaling-your-data-in-order-to-zoom-in-on-small-differences

Exponentially scaling your data in order to zoom in on small differences

Machine learning models benefit from scaling up the area of ​​the scale where most data points show differences. In this blog post, I present an exponential scaler that does just that. It increases the lower or upper end of the scale to focus the machine learning model on the differences that matter most. Design a …

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smote-explained-for-noobs-synthetic-minority-over-sampling-technique-line-by-line

SMOTE explained for noobs – Synthetic Minority Over-sampling TEchnique line by line

Using a machine learning algorithm out of the box is problematic when one class in the training set dominates the other. The Synthetic Minority Resampling Technique (SMOTE) solves this problem. In this tutorial, I’ll explain how SMOTE works, and then how the SMOTE function code works. What problem does SMOTE solve? Machine learning algorithms learn …

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using-geographical-heat-maps-to-visualise-cultural-influence

Using geographical heat maps to visualise cultural influence

Streets, squares and places are usually named after influential people. As a consequence, place names give an idea of ​​who shaped local cultures. In this post, I use open street map data to visualize the impact of various historical figures in Germany. Requesting data from an open street map To find out where geographic features …

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