Tudor Lapusan's Blog

Post info:

Visual interpretation of Decision Tree structure

In Machine Learning it’s important to understand why based on specific inputs (model hyperparameters, features or training set) your models generate some specific outputs (model performance measured by loss functions). My opinion is if we just measure the model performance we will don’t have the full picture of what’s happening behind, so we may end up luckily┬áselecting the set of hyperparameters which we think generate the best model. Maybe the worst thing is that for the next ML project we

Read the full post