Model Representation
Naming Convention:- m = number of training examples
- x = input variables / features
- y = output variable / target variable
- (x, y) = one training example
- (x(i), y(i)) = ith training example
Start with a training set, feed that into our Learning Algorithm. Its the job of the learning algorithm to output a function h called hypothesis which will take x as input. For example:
- Size of house (x) => h => Estimated price
In other words, given a training set, our goal is to learn a function h: X -> Y so that h(x) is a "good" predictor for the corresponding value of y.
There are many ways to represent h, for example: h(x) = a + bx. This is called Linear regression with one variable. Also known as Univariate linear regression.
Mnemonic: My train will xin your ex-wife (xy) at xiyi
- https://www.coursera.org/learn/machine-learning/supplement/cRa2m/model-representation
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