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Advantages and disadvantages of evaluation methods
2022-07-06 10:25:00 【How about a song without trace】
1、 Over fitting : When the learner learns the training samples well , It is possible to take the characteristics of the trained samples as the general properties of all potential samples , This will lead to the decline of Pan China capability ( Generalization ability refers to the ability of the learning model to be applied to unknown samples ).
2、 Under fitting : Low learning ability , I think the general characteristics are all characteristics .
Evaluation methods :
- Set aside method : If the training set contains the vast majority of samples , Then the trained sample may be close to the desired training model , But because of the small test set , The assessment results may not be accurate enough , The pattern of basic partitioned data sets :2:1,4:1 The front is used for training , The latter is used for testing .
- Cross validation : Equal division , Stratified sampling , Take the mean , The defect is : Large data sets are too expensive , Spend more time .
- Self help law : Loop from the overall data into the sample , Put it back again , The final initial data are 0.368 The sample of does not appear , Used for testing . The self-help method can be used to test from the samples that appear in the initial data set , Such a test is also known as out of package estimation . advantage : The self-help method is smaller in the data set , It's hard to divide training effectively \ Test sets are useful , Multiple different training sets can be generated from the initial data set , shortcoming : But it changes the distribution of data sets , This will introduce Estimated deviation .
But when the initial data volume is enough , Set aside method and cross validation method are more commonly used .
Participate in the final parameter model :
General rules of parameter adjustment : Select a range and a varying step size for each parameter , This is a compromise between computational overhead and performance .
Performance metrics : Measure the pan China capability of the model , Performance depends not only on Algorithms and data , It also determines mission requirements .
The most commonly used performance measure for regression tasks : Mean square error .
Recall rate (TP/(TP+FN))、 Precision rate (TP/(TP+FP)):TP Real examples FP False positive example TN True counter example FN False counter example .
F1 It is based on the harmonic average of recall and precision :2*TP/( Total number of samples +TP-TN)
ROC: Characteristics of test work . The horizontal axis TPR( Real examples )=TP/(TP+FN), The vertical axis FPR( False positive example ):FP/(TN+FP).
Normalization : Map values from different ranges of variation to the same fixed range , Common is [0,1], Also known as normalization .
deviation : The difference between the expected output and the real tag , Describe the fitting ability of the learning algorithm itself .
Generalization error can be decomposed into deviation 、 variance ( Have you measured the change of learning performance caused by the change of the same size training set , The impact of data perturbation is characterized )、 And noise ( The lower bound of the expected generalization error that any learning algorithm can achieve in the current task is expressed ) The sum of the .
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