MLP BLOG

Data is not 2D, 3D or 4D, it is more complicated and difficult to visualize

F1 score get a boost of up to 79% for the most important class in a support vector machines classification problem. Positive curvature of no

In part I it was explained that a first sight at the f1 scores -considering features as in a classic 3D space against a non-euclidean one- g

The Hyperspace is not Always Euclidean: Study of f1 Score with Sklearn Metrics, Grid Search - Part III (Google Colab)

...this sparse matrix that contains all classes with the most influence, is compared against the 'y_true' that needs also to be...

Each frequency bin is treated as coordinate feature, then, the hyperplane has as many dimensions as classes are.