In a previous couple of articles, we specifically focused on the performance of machine learning models. First, we talked about how to quantify machine learning model performance and how to improve it with regularization . Then we covered the other optimization techniques, both basic ones like Gradient Descent  and advanced ones , like Adam. Finally, we were able to see how to perform hyperparameter optimization  and get the best “configuration” for your model. However, what we haven’t considered so far is how we can improve performance by making modifications in the data itself. We were focused on the model. So far in our articles about SVM and clustering , we applied some techniques (like scaling) to our data, but we haven’t done a deeper analysis of this process and how manipulations with the dataset can help us with performance improvements. In this article we do exactly that, explore the most effective feature engineering techniques, that are often required in order to get good results.

#ai #machine learning #python #artificial intelligence #machine learning #software craft #software craftsmanship #software development