It is a summary of classification problem prediction results.
The number of inaccurate and right predictions is totaled and divided by class using count values. This is the answer to the confusion matrix’s riddle.
The confusion matrix displays the various ways in which the classification model is perplexed when making predictions.
This breakdown overcomes the limitation of relying solely on classification accuracy.
The definition of classification accuracy is the ratio of accurate classification to total classifications produced. It is frequently expressed as a percentage by dividing the result by 100.
Classification accuracy can also be easily converted into a misclassification rate or error rate by inverting the value, as in:
Although classification accuracy is a good place to start, it frequently runs into issues in practice. Prime examples of these are:
This highlights why accuracy in Machine Learning isn’t always the best metric for classifier performance, especially when working with classes that are much more frequent than others (skewed dataset).
The precision of classification can obscure the information you need to diagnose your model’s performance. However, we may use a confusion matrix accuracy to separate away this detail.
The neural network confusion matrix python function calculates a confusion matrix using an array or list of expected values and a list of predictions from your machine learning model and returns the result as an array. After that, you may print this array and analyze the results.
The matrix summarizing the outcomes for the contrived 2 class problem is printed when this example is run.
Calculating a confusion matrix can help you understand what your classification model gets right and what types of errors it makes.
The method of calculating the confusion matrix contains a few steps:
These numbers are then structured into a table, or matrix.
Expected in the future: The matrix’s rows each correspond to a predicted class.
Predictions from the top: Each column of the matrix represents a real-world class.
The correct and incorrect classification counts are then entered into the table.
The total number of correct predictions for a class are entered into the expected row and predicted column for that class value.
Similarly, the sum number of wrong predictions for a class are entered into the expected row and predicted column for that class value.
This matrix is useful for two-class problems because it is simple to understand. However, it can be extended to complications with more class values.
It’s a performance metric for machine learning classification problems with two or more classes as output.
It’s great for determining Recall, Precision, Specificity, Accuracy, and, most crucially, the AUC-ROC Curve.
The model’s overall accuracy is similar and high when using both the train and test data sets. Even the metrics at the class level are similar and high.
We may conclude that the SVC model has been properly calibrated and is capable of making accurate predictions on the test data set in terms of both general and class-level accuracy.
Essentially, a confusion matrix can help machine learning classification models perform better and faster.