True negative is the case wherein the predicted class is ‘False’, and the actual class to which the model belongs to is also ‘False’. Considertwo classes namely ‘True’ and ‘False’. A non-spam email was correctly identified as ‘non-spam’. True positive rate, which is also known as sensitivity or recall can be defined as the ratio of truepositives and sum of true positives and false negatives: TPR = True positives/ (True positives + False negatives) True negatives: This also can be understood with respect to a binary classification example. True positive is the case wherein the predicted class is ‘True’ and the actual class to which the data item belongs to is also ‘True’. It is used with classification problems wherein the output (or the class into which the dataset has been classified into) could belong any of the classes (one of the 2 in binary classification and one of the many in multi-class classification). The confusion matrix is not a performance measure on its own, but most of the performance metrics are based on this matrix and the value this matrix gives out. Terminologies associated with the confusion matrix: True positives: Let us understand this with respect to a binary classification example- There are 2 classesnamely, True and False. Precision and Recall: It depends on problem statement.Machine Learning Tutorial By KnowledgeHut It is one of the simplest metrics that helps in finding how correct and how accurate the model is.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. 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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