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# Hypothesis

X denotes the input variables, also called input features, and y denotes the output or target variable that we are trying to predict. The pair (x, y) is called a training example, and the dataset used to learn is a list of m training examples, where {(x, y)} is a training set. We will also use X to denote the space of input values, and Y to denote the space of output values. For a training set, to learn a function, h: X → Y so that h(x) is a predictor for the value of y. Function h is called a hypothesis.

When the target variable to be predicted is continuous, we call the learning problem a regression problem. When y can take a small number of discrete values, we call it a classification problem.

Let's say we choose to approximate ...

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