Classification Tutorial: Linear and Non-Linear Models with Heart Disease Dataset Introduction Classification is a machine learning task where we assign a category (class) to an input based on its features. For example, predicting whether a patient has heart disease (yes/no) based on medical data. Classification algorithms are divided into: Linear Models : Assume classes can be separated by a straight line (or hyperplane in higher dimensions). Non-Linear Models : Capture complex, curved boundaries between classes. For Beginners : Think of classification like sorting fruits into apples and oranges. Linear models draw a straight line to separate them, while non-linear models draw wiggly lines to handle trickier cases where apples and oranges are mixed in complex patterns. For Professionals : Classification involves learning a function ( f(X) \to y ), where ( X ) is the feature matrix and ( y ) is the class label (e.g., 0 or 1). Linear models assume ( f ) is a linear combinat...
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