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# Regression &amp; Classification

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### Regression &amp; Classification

1. 1. 1. Machine Learning  Supervised Learning  Regression  Classification  Unsupervised Learning  Clustering2. Regression/Classification  Regression  Linear Regression with One Variable  Linear Regression with Multiple Variables  Classification  Logistic Regression2.1 Linear Regression with One Variable2.1.1 Model Representationunivariate linear regression(Linear regression with one variable) : Univariate linear regressionis used when you want to predict a single output value from a single input value.2.1.2 The Hypothesis FunctionGeneral form :Linear Regression은 input data(즉, x)를 output data(즉, y)로적절하게매핑하는함수 h를만드는것이라고할수있다.Example:x (input) y (output)0 41 72 73 8theta0=2, theta1=2라고하면 h(x) = 2 + 2x 가된다.input이 1일때추정값(예측치) y는 4가되고실측치와의차이는 3이다.
2. 2. 2.1.3 Cost FunctionWe can measure the accuracy of our hypothesis function by using a cost function. m개의input data가있을때각 input data에대한예측치 h(x)와실측치 y의평균차이값을최소화하는 theta가해당 input과 output을가장잘표현하는모델의 parameter가된다.Cost function은다음과같다. ※ 1/2을곱하는이유는차후계산식에서수학적으로표현이쉽기때문이다.Cost function을통해얻고자하는 goal은J()를 theta0과 theta1에대해서그려보면아래와같다.