What is a confusion matrix?

The confusion matrix is a 2×2 table that contains 4 outputs provided by the binary classifier. Various measures, such as error-rate, accuracy, specificity, sensitivity, precision and recall are derived from it. Confusion matrix.

A dataset used for performance evaluation is called a test data set. It should contains the correct labels and predicted labels.

The predicted labels will exactly the same if the performance of a binary classifier is perfect.

The predicted labels usually match with part of the observed labels in real-world scenarios.

A binary classifier predicts all the data instances of a test data set as either positive or negative. This produces for outcomes-

True positive (TP) – Correct positive prediction
positive (FP) – Incorrect positive prediction
True negative (TN) – Correct negative prediction
False negative (FN) – Incorrect negative prediction

Basic measures derived from the confusion matrix-

Error Rate – (FP + FN) / (P + N)
Accuracy – (TP + TN) / (P + N)
Sensitivity (Recall or True positive rate) – TP / P
Specificity (True negative rate) – TN / N
Precision (Positive predicted value) – TP / (TP + FP).

Author

Naveen

Naveen Pandey has more than 2 years of experience in data science and machine learning. He is an experienced Machine Learning Engineer with a strong background in data analysis, natural language processing, and machine learning. Holding a Bachelor of Science in Information Technology from Sikkim Manipal University, he excels in leveraging cutting-edge technologies such as Large Language Models (LLMs), TensorFlow, PyTorch, and Hugging Face to develop innovative solutions.
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