machine learning metrics cheat sheet

classification model, which gives us a better evaluation value and gives us a more direct way to evaluate the good and bad of the model. One last thing to keep in mind, in the definition of precision and recall, we define precision and recall rates, and we habitually use Y=1 to show that this class appears very little. So if we try to detect a very rare situation, like cancer. I hope it's a rare situation where precision and recall are defined as Y=1 rather than y=0, as some of the fewer classe

Skewed classesSkewed classes: The number of a species is much higher (or less than) another class, that is, two extreme cases.Predicting the classification model of cancer, if there is only a 1% classification error on test set, at first glance is a good result, actually if we predict all Y to be 0 (that is, none is cancer), the classification error is 0.5% (because the ratio of cancer is 0.5%). The error is reduced, is this an improvement to the algorithm? Obviously not. Because the latter meth

The generalization performance of the learner is evaluated. In the measure, we should have the evaluation criterion to measure the generalization ability of the model. Performance metrics reflect task requirements, and using different performance metrics often leads to different judgments when comparing the capabilities of different models, which means that the model is relatively good or bad, and that the

as:If the covariance matrix is a unit matrix (the independent distribution of each sample vector), the formula becomes:That's the Euclidean distance.If the covariance matrix is a diagonal matrix, the formula becomes the normalized Euclidean distance.(2) The advantages and disadvantages of Markov distance: dimension independent, exclude the interference between the correlations between variables.(3) MATLAB calculation (1 2), (1 3), (2 2), (3 1) of the Markov distance between 22X = [1 2; 1 3; 2 2

1. Confusion Matrixis a confusion matrix of two types of problems in which the output uses a different category labelCommonly used metrics to measure classification performance are: The correct rate (Precision), which is equal to tp/(TP+FP), gives the ratio of the true positive example in the sample that is predicted to be a positive example. recall Rate (Recall), which he equals to tp/(TP+FN), gives the true positive example of the predi

Evaluation algorithm Excellent program, commonly used a series of indicators to measure, mainly including: Precision,recall,f-1 score, why design these values? Can't I use precision alone?1, what is precision?Precison, accuracy, mainly indicates how much of the detected alert is the correct judgment (True POSITIVE,TP).In practice, due to the different proportions of normal and abnormal data in the sample, accuracy can not reflect the real algorithm performance, for example:Cancer detection: It i