Confusion Matrix Simplified for Beginners 2022 | Machine Learning Tutorial | SCALER

What is the Confusion Matrix?
A confusion matrix is a tool for predictive analysis. It’s a table that compares predicted values with actual values. In the machine learning context, a confusion matrix is a metric used to quantify the performance of a machine learning classifier. The confusion matrix is used when there are two or more classes as the output of the classifier.

Confusion matrices are used to visualise important predictive analytics like recall, specificity, accuracy, and precision. Confusion matrices are useful because they give direct comparisons of values like True Positives, False Positives, True Negatives and False Negatives. In contrast, other machine learning classification metrics like “Accuracy” give less useful information, as Accuracy is simply the difference between correct predictions divided by the total number of predictions.

The confusion matrix is considered to be one of the most powerful tools for predictive analysis in machine learning.

The following topics are covered in this confusion matrix tutorial:

0:00 - Introduction
0:05 - Understanding confusion matrix
0:29 - Problem statement
4:55 - Model predictions
6:17 - Confusion model

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[ricktikra]

This is so good and so clear. Thank you so much!

[sohailshaiwale]

thank you so much, this is exactly what I was looking for