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Regression:

• Stock Market Forecast
• Self-driving Car
• Recommendation
• Estimating the Combat Power of a pokemon after evolution

Function:

f( pokemon ) = CP after evolution

Method:

Step1:Model Linear model：f1: y = b + w * xcp (w and b are parameters，xcp is CP before evolution)【w is weight, b is bias】 Another Model：y = b + w1 * xcp + w2 * xcp^2 … Redesign the model：different species have different function（引入seg函数） … Step2:Goodness of Function Training Data Loss function L：L(f) = L(w,b)【how bad a funtion is（此处为方差）】 Regularization： Step3:Best Funtion A set of function -> Goodness of function <- Training Data Gradient Descent：偏微分

Overfitting：

A more complex model yields lower error on training data. A more complex model does not always lead to better performance on testing data.

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