Demystifying Hyperparameter Tuning in Machine Learning If you’ve ever tried to bake a cake, you know how important it is to get the right amount of ingredients. Too much sugar, and it’s too sweet. Too little baking powder, and it won’t rise. Machine learning is quite similar! Here, the “ingredients” are called hyperparameters. What are Hyperparameters? In simple words, hyperparameters are settings that you choose before training your machine learning model. They are not learned from the data, but you have to set them yourself. For example, if you are using a decision tree, you might set the maximum depth of the tree. If you are training a neural network, you might set the number of layers or how fast the model learns (learning rate). Why is Tuning Important? Imagine you are tuning a radio to get the clearest sound. If you don’t tune it properly, you’ll only get noise. Similarly, if you don’t set the right hyperparameters, your model might not perform w...