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...
Explainable AI Introduction to Explainable AI (XAI) Over the past couple of years, Artificial Intelligence (AI) has progressed very rapidly in a wide range of sectors, from healthcare to finance, and from education to entertainment. As AI technologies continue to grow, their data-driven decision-making becomes increasingly stronger. However, the complexity of such algorithms, particularly those derived from deep learning models, has the tendency to veil the decision-making process from both users and developers. This transparency of AI systems has created an immediate need for so-called "Explainable AI" (XAI). Source: Google Images What is Explainable AI? Explainable AI, or XAI, refers to a branch of artificial intelligence that seeks to promote the transparency, interpretability, and explainability of the behavior of AI systems to human stakeholders. While AI models such as deep neural networks and ensemble methods can make extremely accurate predictions, their "black b...