Applied Data Science Coding with Python: How to get Classification Confusion Matrix

A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. The matrix is used to visualize the model’s predictions and compare them to the actual outcomes, and it is a useful tool to understand the model’s performance.

In Python, the scikit-learn library provides a built-in function called “confusion_matrix” that can be used to calculate the confusion matrix of a model. To use it, you first need to import the function from the library and then pass in two arguments: the predicted class labels and the true class labels.

The predicted class labels are the outputs generated by the model and the true class labels are the actual class labels of the data points. The function will then return a matrix of integers, where each row corresponds to an actual class and each column corresponds to a predicted class.

The diagonal elements of the matrix represent the number of correct predictions that the model made for each class, while the off-diagonal elements represent the number of incorrect predictions. For example, True Positives (TP) represent the number of times the model correctly predicted the positive class, False Positives (FP) represent the number of times the model incorrectly predicted the positive class, and so on.

Confusion matrix can also be used to calculate other performance metrics like precision, recall, F1-score, which are useful in understanding the model’s performance.

In summary, the confusion matrix is a useful tool to understand the performance of a classification model, it helps to visualize the model’s predictions and compare them to the actual outcomes. Python’s scikit-learn library provides a built-in function called “confusion_matrix” that can be used to calculate the confusion matrix of a model and also calculate other performance metrics like precision, recall, F1-score.

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get Classification Confusion Matrix.