shadowsockr安卓版下载

    
    

    
    
    

    
    
    

    
    






    
    
    

    
    

        

            

                

                    
                    

                        Confusion Matrix
                        

                        Online Calculator
                    


                    
                    

                        

                            


                                

                                    
                                        

                                            
                                            
                                                 True Positive 
                                            
                                            
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                                                 Predicted Positive 
                                            
                                            
                                                

                                                    
                                                    
                                                

                                            		
                                            
                                                

                                                    
                                                    
                                                

                                            		
                                        

                                        

                                            
                                                 Predicted Negative 
                                            
                                            
                                                

                                                    
                                                    
                                                

                                            		
                                            
                                                

                                                    
                                                    
                                                

                                            		
                                        

                                    
                                


                            

                        

                    

                    
                    

                        Calculate
                    


                    
                    

                    
                    

                        

                            
                                

                                    Measure
                                    Value
                                    Derivations
                                

                            
                            
                                

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shadowsockr安卓版下载

                    

                        

                            A Confusion Matrix is a popular representation of the performance of
                            classification models. The matrix (table) shows us the number of correctly
                            and incorrectly classified examples, compared to the actual outcomes
                            (target value) in the test data. One of the advantages of using confusion
                            matrix as evaluation tool is that it allows more detailed analysis
                            (such as if the model is confusing two classes), than simple proportion
                            of correctly classified examples (accuracy) which can give misleading
                            results if the dataset is unbalanced (i.e.  when there are huge differences
                            in number of between difference classes).
                        

                        

                            The matrix is n by n, where n is the number of classes. The simplest
                            classifiers, called binary classifiers, has only two classes:
                            positive/negative, yes/no, male/female…
                            Performance of a binary classifier is summarized in a confusion matrix
                            that cross-tabulates predicted and observed examples into four options:
                            
    
                                
  • 
                                    True Positive (TP): Correctly predicting a label
                                    (we predicted “yes”, and it’s “yes”),
                                
    • 
                                
  • 
                                    i7加速器官网 Correctly predicting the other label
                                    (we predicted “no”, and it’s “no”),
                                
    • 
                                
  • 
                                    False Positive (FP): Falsely Predicting a label
                                    (we predicted “yes”, but it's “no”),
                                
    • 
                                
  • 
                                    False Negative (FN): Missing and incoming label
                                    (we predicted “no”, but it’s “yes”).
                                
    • 
                            

                        

                    

                

            

        

    


    
    

        

            

                

                    
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shadowsockr安卓版下载

                    
                    

                        
                        

                            How can we use those metrics and what we can read from the
                            confusion matrix?  For instance, let's consider a classical
                            problem of predicting spam and non-spam email, by using binary
                            classification model.  Our dataset consists of 50 emails that
                            are Spam, and 105 emails that are Not Spam.  In order to evaluate
                            the performance of our developed model, which labels emails as
                            Spam or Not Spam, we can use confusion matrix, where the outcome
                            is formulated in a 2×2 
                            
                            contingency table or a
                            
                                confusion matrix
                            :
                        

                        

                            
    
                                
  • 
                                    Altogether, the classifier made 100 predictions
                                    (100 emails were classified in Spam or Non-Spam class)
                                
    • 
                                
  • 
                                    Out of 100 emails, our model correctly classified 95 emails: 
                                    85 were correctly classified as Non-Spam, 
                                    and 10 of them were correctly classified as Spam.
                                    This result to 
                                    如何进入外国网站.
                                
    • 
                                
  • 
                                    Further, 5 out of 100 emails were 如何上外国网站: 
                                    5 emails, which were actual Spam, were not predicted as Spam (False Negative). 
                                    And more important, no email was falsely predicted as Spam (怎样浏览国外网站), 
                                    which is very desired in this case. 
                                
    • 
                                
  • 
                                    We can observe that our model is very conservative when it comes to  predicting Spam.
                                    Therfore, the 
                                    precision of this of this model is very high: 1.0.
                                
    • 
                                
  • 
                                    By computing 
                                    additional measures (also called rates) from the classification 
                                    matrix, we can get additional insight about our model.
                                
    • 
                            

                        

                        
                        

                            Try it out!
                        

                    

                

            

        

    


    
    

        

            

                

                    
shadowsockr安卓版下载

                    

                        
                            

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