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In machine learning and deep learning, the concepts of Model vs Instance Models and Train-Test Split are closely intertwined. A model serves as the blueprint for learning patterns from data, while an instance model represents the specific realization of that blueprint after training. The train-test split, on the other hand, plays a critical role in the creation and evaluation of these instance models by dividing the dataset into subsets for training and testing. This blog post will delve into the relationship between these concepts,   first we explain model vs instance based and then we explain train- test spilt and provide two great examples to understand all we have explained better. These basics is mandatory to understand machine learning better:    Understanding Model-Based & Instance-Based Learning in Machine Learning Machine learning is a transformative technology that relies on various methods to teach computers how to learn from data and make predictions. Two fundamental approaches in this domain are model-based learning and instance-based learning. This blog post delves into these two learning paradigms, their differences, and how they relate to common issues like overfitting and underfitting. We will also explore how deep learning fits into this framework. Model-Based Learning Definition: Model-based...