Confusion Matrix

What is a Confusion Matrix?

A confusion matrix is a table often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. It is one of the most intuitive and easiest metrics to understand and interpret. The matrix compares the actual target values with those predicted by the machine learning model. This comparison produces a matrix with four different combinations of predicted and actual values, each representing different types of correct and incorrect predictions made by the model.

The confusion matrix itself is simple to understand, but the related terminology can be confusing. To make it more comprehensible, let's define each term:

  • True Positives (TP): These are cases in which we predicted yes (the event occurred), and they actually do belong to the positive class.
  • True Negatives (TN): We predicted no, and they don’t belong to the positive class.
  • False Positives (FP)

    : We predicted yes, but they don’t actually belong to the positive class. (Also known as a "Type I error.")

  • False Negatives (FN)

    : We predicted no, but they actually do belong to the positive class. (Also known as a "Type II error.")

Structure of a Confusion Matrix

A typical confusion matrix looks like the table below:

Predicted: No Predicted: Yes
Actual: No TN FP
Actual: Yes FN TP

Metrics Derived from a Confusion Matrix

Several performance metrics can be computed from a confusion matrix:

  • Accuracy: Overall, how often is the classifier correct? Calculated as (TP+TN)/(TP+TN+FP+FN).
  • Precision: When it predicts yes, how often is it correct? Calculated as TP/(TP+FP).
  • Recall (Sensitivity or True Positive Rate): How often is the prediction correct when it's actually yes? Calculated as TP/(TP+FN).
  • F1 Score

    : Harmonic mean of precision and recall. Calculated as 2*(Precision*Recall)/(Precision+Recall).

  • Specificity (True Negative Rate): How often is the prediction correct when it's actually no? Calculated as TN/(TN+FP).

Example Calculation of Confusion Matrix

Let's assume we have a binary classification problem to predict whether a tumor is malignant (positive class) or benign (negative class). After running the classification model, we get the following results:

  • True Positives (TP): 50
  • True Negatives (TN): 100
  • False Positives (FP): 10 (Type I error)
  • False Negatives (FN): 5 (Type II error)

From these values, we can calculate the metrics as follows:

  • Accuracy = (TP+TN)/(TP+TN+FP+FN) = (50+100)/(50+100+10+5) = 150/165 ≈ 0.909
  • Precision = TP/(TP+FP) = 50/(50+10) = 50/60 ≈ 0.833
  • Recall = TP/(TP+FN) = 50/(50+5) = 50/55 ≈ 0.909
  • F1 Score = 2*(Precision*Recall)/(Precision+Recall) = 2*(0.833*0.909)/(0.833+0.909) ≈ 0.869
  • Specificity = TN/(TN+FP) = 100/(100+10) = 100/110 ≈ 0.909

Importance of Confusion Matrix in Machine Learning

The confusion matrix is a crucial diagnostic tool in machine learning because it not only gives you insight into the errors being made by your classifier but also types of errors that are being made. This breakdown helps you to overrule the accuracy paradox (where a model has a high accuracy rate but is still making a lot of errors) and to focus on the important areas of improvement in your classifier.

For instance, in medical diagnosis, a false negative might be more serious than a false positive. The confusion matrix will help you to see if your model is getting them right, and if not, it will show you what kind of mistakes are being made.


The confusion matrix is a powerful tool for summarizing the performance of a classification algorithm. Understanding the confusion matrix and the metrics that can be derived from it is essential for evaluating and improving your machine learning models. By using the confusion matrix as a reference, you can fine-tune your models to reduce the number of false positives and false negatives, thereby increasing the model's predictive power.


  • Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.

  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
  • He, H., & Garcia, E. A. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263-1284.

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