Machine Learning – Confusion Matrix

Hello Viabyte! In the world of machine learning, confusion matrix is one of the most important evaluation tools. It helps to evaluate the performance of a machine learning algorithm by comparing the predicted results with the actual results. In this article, we will discuss the concept of confusion matrix, its components, and how it can be used to evaluate the performance of a machine learning algorithm.

What is a Confusion Matrix?

A confusion matrix is a table that is used to evaluate the performance of a classification model. It consists of four components: true positive, true negative, false positive, and false negative. These components help in determining the accuracy, precision, recall, and F1 score of a model.

Components of Confusion Matrix

Let’s understand the components of a confusion matrix:

• True Positive (TP) – The number of correct positive predictions made by the model.
• True Negative (TN) – The number of correct negative predictions made by the model.
• False Positive (FP) – The number of incorrect positive predictions made by the model.
• False Negative (FN) – The number of incorrect negative predictions made by the model.

How to Create a Confusion Matrix?

To create a confusion matrix, you need to have actual and predicted values. Let’s consider the example of a binary classification problem where we need to predict whether a customer will buy a product or not. We have the following actual and predicted values:

Actual Value	Predicted Value
1	1
0	1
0	0
1	0

Step-by-Step Procedure to Create a Confusion Matrix

The following steps can be followed to create a confusion matrix:

1. Create a 2×2 matrix with the rows and columns representing the actual and predicted values.
2. Count the number of true positives, true negatives, false positives, and false negatives.
3. Fill in the values in the matrix.

Using the example values above, the confusion matrix would look like:

	Predicted 0	Predicted 1
Actual 0	1 (True Negative)	1 (False Positive)
Actual 1	1 (False Negative)	1 (True Positive)

Evaluation Metrics

Now that we have created a confusion matrix, we can use it to calculate various evaluation metrics such as accuracy, precision, recall, and F1 score.

• Accuracy – It measures the ratio of correct predictions to the total number of predictions made by the model.

To calculate accuracy, we can use the following formula:

Accuracy = (TP + TN) / (TP + TN + FP + FN)

Using the values from our example confusion matrix, the accuracy would be:

Accuracy = (1 + 1) / (1 + 1 + 1 + 1) = 0.5

• Precision – It measures the ratio of true positives to the total number of positive predictions made by the model.

To calculate precision, we can use the following formula:

Precision = TP / (TP + FP)

Using the values from our example confusion matrix, the precision would be:

Precision = 1 / (1 + 1) = 0.5

• Recall – It measures the ratio of true positives to the total number of actual positive values.

To calculate recall, we can use the following formula:

Recall = TP / (TP + FN)

Using the values from our example confusion matrix, the recall would be:

Recall = 1 / (1 + 1) = 0.5

• F1 Score – It is the harmonic mean of precision and recall.

To calculate F1 score, we can use the following formula:

F1 Score = 2 * (Precision * Recall) / (Precision + Recall)

Using the values from our example confusion matrix, the F1 score would be:

F1 Score = 2 * (0.5 * 0.5) / (0.5 + 0.5) = 0.5

Use of Confusion Matrix in Machine Learning

Confusion matrix is a very useful tool in machine learning. It helps in evaluating the performance of a machine learning algorithm and identifying its strengths and weaknesses. It can be used to compare the performance of different models and to select the best one for a given task.

Moreover, confusion matrix can be used to identify the type of errors made by a model. For example, if a model has a high false negative rate, it means that it is failing to identify positive instances. On the other hand, if it has a high false positive rate, it means that it is wrongly identifying negative instances as positive.

Example Code

Let’s consider an example of how to use confusion matrix in Python. We will use the scikit-learn library to create a confusion matrix.

from sklearn.metrics import confusion_matrix

# Actual Values
y_true = [1, 0, 0, 1, 1, 0, 1]

# Predicted Values
y_pred = [1, 0, 1, 1, 0, 0, 1]

# Create a confusion matrix
confusion_matrix(y_true, y_pred)

Output :

array([[2, 1],
       [1, 3]])

The output shows a 2×2 confusion matrix with 2 true positives, 1 false positive, 1 false negative, and 3 true negatives.

Conclusion

In conclusion, confusion matrix is a very important tool in machine learning. It helps in evaluating the performance of a classification model and identifying its strengths and weaknesses. Moreover, it can be used to identify the type of errors made by a model and to compare the performance of different models. By understanding the concept of confusion matrix and its components, we can evaluate the performance of a machine learning algorithm more effectively.