![]() F1 score is even more unreliable in such cases, and here would yield over 97. The overall accuracy would be 95%, but in more detail the classifier would have a 100% recognition rate ( sensitivity) for the cancer class but a 0% recognition rate for the non-cancer class. Accuracy will yield misleading results if the data set is unbalanced that is, when the numbers of observations in different classes vary greatly.įor example, if there were 95 cancer samples and only 5 non-cancer samples in the data, a particular classifier might classify all the observations as having cancer. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy). In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. Sensitivity, recall, hit rate, or true positive rate (TPR) T P R = T P P = T P T P + F N = 1 − F N R. True positive (TP) A test result that correctly indicates the presence of a condition or characteristic true negative (TN) A test result that correctly indicates the absence of a condition or characteristic false positive (FP) A test result which wrongly indicates that a particular condition or attribute is present false negative (FN) A test result which wrongly indicates that a particular condition or attribute is absent Precision and Recall: It depends on problem statement.Table layout for visualizing performance also called an error matrix Terminology and derivationsįrom a confusion matrix condition positive (P) the number of real positive cases in the data condition negative (N) the number of real negative cases in the data.Accuracy: When you have Balanced Dataset.F1 score: It is weighted average of precision and recall.įormula: F1 score= 2 (PrecisionRecall) / (Precision + Recall)Īlso read: A Guide to Principal Component Analysis (PCA) for Machine Learning When to use Accuracy, Precision, Recall and F1 score? Recall: It gives answer to the question: Out of total actual positive values, how many positives were predicted correctly.Ĥ. Precision: It gives answer to the question: Out of total predicted positive results, how many results were actually positive?ģ. A confusion matrix, also known as an error matrix, is a powerful tool used to evaluate the performance of classification models. Accuracy: Number of correct Predictions/Total number of predictionsįormula: accuracy=TP+TN / (TP + FP+TN+FN)Ģ.We can extract important parameters from the confusion matrix. Read this article: K-Nearest Neighbor (KNN) Algorithm in Machine Learning using Python So we can conclude that – Out of a total of 100 predictions, Let us create the confusion matrix for this. Our model also predicted 30 as ‘not sick’, out of which 16 are actually ‘not sick’ and 14 are ‘sick’. We know that 74 out of 100 are ‘sick’ and 26 out of 100 are ‘not sick’.īut our model predicted that 70 are ‘sick’, out of which 60 are actually ‘sick’ and the rest 10 are ‘not sick’. Let’s understand this with an example: Suppose, we have blood reports of 100 patients and we have to find out if they are sick or not. A type II error happens when you accept the null hypothesis (as true) when it, in reality, is false, which by convention corresponds to a false negative. ![]() Corresponding to true positive and false positive terminology, a type I error follows when you reject the null hypothesis (as false) when it is actually true, which by custom corresponds to a false positive. Refer this article: Support Vector Machine Algorithm (SVM) – Understanding Kernel TrickĪnalogy with statistics: There is the type I errors and type II errors in statistics. ![]()
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