One post tagged with "Hyperparameters"

Introduction​

In machine learning, achieving the right balance between model complexity and predictive performance is crucial. A key concept in understanding this balance is the Bias-Variance Tradeoff, which defines how well a model generalizes to unseen data. Along with this, the No Free Lunch Theorem provides an essential principle that explains the limitations of machine learning models. Together, these concepts form the foundation of understanding how machine learning models perform across various domains.

This article will explore the Bias-Variance Tradeoff, the implications of the No Free Lunch theorem, and how to address issues like underfitting, overfitting, and regularization in machine learning models.