Confusion Matrix in Machine Learning
A confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known. It is a common tool in machine learning to evaluate the performance of a classification algorithm.
Understanding Confusion Matrix
The confusion matrix is a 2×2 matrix that contains four important metrics:
- True Positives (TP) – The number of correct predictions that an instance is positive.
- False Positives (FP) – The number of incorrect predictions that an instance is positive.
- True Negatives (TN) – The number of correct predictions that an instance is negative.
- False Negatives (FN) – The number of incorrect predictions that an instance is negative.
These metrics help us to evaluate the performance of a classification model by calculating metrics such as accuracy, precision, recall, and F1-score.
Confusion Matrix with Python
In Python, we can easily create a confusion matrix using libraries such as scikit-learn. Here’s a simple example of creating a confusion matrix:
from sklearn.metrics import confusion_matrix
# True values
y_true = [1, 0, 1, 1, 0, 0]
# Predicted values
y_pred = [1, 1, 1, 0, 0, 1]
# Create confusion matrix
cm = confusion_matrix(y_true, y_pred)
print(cm)
This code snippet will output the confusion matrix for the given true and predicted values.
Conclusion
Understanding the confusion matrix is essential in evaluating the performance of a classification model in machine learning. By analyzing the metrics provided by the confusion matrix, we can improve our model and make informed decisions.
