摘要： 1. Precisoin and recall precision is how precise i am at showing good stuff on my website recall is how good i am at find all the postive reviews prec 阅读全文

摘要： 1. Ensemble classifier Each classifier votes on prediction Ensemble model = sign(w1f1(xi) + w2f2(xi) + w3f3(xi)) w1 w2 w3 is the learning coefficients 阅读全文

摘要： 1. Quality metric Quality metric for the desicion tree is the classification error error=number of incorrect predictions / number of examples 2. Greed 阅读全文

摘要： 1. Linear classifier It will use training data to learn a weight or coefficient for each word. We use the gradient ascent to find the best model with 阅读全文

摘要： 1. Fit locally If the true model changes much, we want to fit our function locally to different regions of the input space. 2. Scaled distance \ we pu 阅读全文

摘要： 1. Feature selection Sometimes, we need to decrease the number of features Efficiency: With fewer features, we can compute quickly Interpretaility: wh 阅读全文

摘要： 1. Ridge regression A way to automatically balance between bias and varaince situations and regulate overfitting when using many features. because the 阅读全文

摘要： 1. Training Error Define a loss funtion like below: and the train error is defined as theaverage loss on houses in training set: and RMSE is simply th 阅读全文

摘要： 1. Modeling seasonality w1 models the linear trend of the overall process. w2 models the seasonal component sinusoid with a period of 12 and you do no 阅读全文

摘要： 1. Convex and concave functions Concave is the upside-down of the convex function and convex is a bow-shaped function 2. Stepsize common choice: as th 阅读全文