Dipayon Paul

The bias-variance tradeoff is a central concept in supervised machine learning that describes the relationship between a model's complexity, its ability to fit the training data, and its ability to generalize to unseen data. Understanding this tradeoff is crucial for choosing the right model and achieving optimal performance.

In machine learning, the ultimate goal is to build models that generalize well to unseen data. We don't just want a model that performs well on the data it was trained on; we want a model that can accurately predict outcomes on new, real-world data. To achieve this,

Machine learning (ML) is a powerful field that enables computers to learn from data without explicit programming. Within ML, there are three fundamental learning paradigms: supervised learning, unsupervised learning, and reinforcement learning. Understanding the distinctions between these approaches is crucial for choosing the right technique for a given problem and