Contents
How is confusion matrix used in machine learning?
Confusion matrix is used to evaluate the correctness of a classification model. In this blog, we will be talking about confusion matrix and its different terminologies. We will also discuss different performance metrics classification accuracy, sensitivity, specificity, recall, and F1 score.
How to calculate the accuracy of the confusion matrix?
In this example, TN = 90, FP = 0, FN = 10 and TP = 0. The confusion matrix is as follows. Figure 7: Confusion matrix for healthy vs unhealthy people classification task. Accuracy in this case will be (90 + 0)/ (100) = 0.9 and in percentage the accuracy is 90 %.
Is the confusion matrix A subset of artificial intelligence?
It is seen as a subset of artificial intelligence. Classification is a process of categorizing a given set of data into classes.
What does each row in a confusion matrix mean?
Each row in a confusion matrix represents an actual class, while each column represents a predicted class. For more info about the confusion matrix click here. The confusion matrix gives you a lot of information, but sometimes you may prefer a more concise metric. Precision. precision = (TP) / (TP+FP)
This is a brief idea about how we develop a machine learning model. Before finalising the classifier model, we have to be sure if it is performing well or not. Confusion Matrix measures the performance of a classifier to check efficiency and precision in predicting results.
What is the accuracy of the confusion matrix?
Now we can also see all the four terms used in the above confusion matrix. Now we will find all the above-defined performance metrics from this confusion matrix. = 3/5 which is 60%. So, the accuracy from the above confusion matrix is 60%. = 1 / (1+1)
How are class statistics calculated in confusion matrix?
And the class statistics are calculated on top of the confusion matrix. The confusion matrix and class statistics are displayed in the interactive view of the Scorer (JavaScript) node as shown in Figure 2. Fig. 2: Confusion matrix and class statistics in the interactive view of the Scorer (JavaScript) node.
How to calculate a confusion matrix in Python?
The scikit-learn library for machine learning in Python can calculate a confusion matrix. Given an array or list of expected values and a list of predictions from your machine learning model, the confusion_matrix () function will calculate a confusion matrix and return the result as an array.