Confusion Matrix Analyzer – Free Online ML Tool

Confusion Matrix Analyzer

Interactive classification diagnostics for binary and multiclass models. Explore performance, class-level behavior, errors, confusion pairs and automatically generated ML insights.

Matrix Input

Class Labels Confusion Matrix 85,5,10 4,90,6 8,7,85

Rows = actual classes
Columns = predicted classes

Example:
85,5,10
4,90,6
8,7,85

Display

Normalization Raw Counts Normalize by Actual Class Normalize by Predicted Class Normalize Overall Decimal Places 0 1 2

Interpretation Thresholds

Excellent ≥ Warning below

These thresholds control how class performance is described in the report.

Model Overview

Class	Support	Precision	Recall	Specificity	F1	Error Rate

Runs entirely in your browser. Matrix data is not uploaded anywhere.

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Kovendo’s free Confusion Matrix Analyzer and online confusion matrix calculator helps students, researchers, and machine learning practitioners analyze binary and multiclass classification models. Explore confusion matrix accuracy, precision, recall, F1 score, class-level performance, prediction errors, confusion pairs, and automated model insights.

What Is a Confusion Matrix Analyzer?

A confusion matrix analyzer is an online machine learning tool used to evaluate the performance of a classification model. It compares the actual class labels with the predicted labels and helps identify correct predictions, false positives, false negatives, and other classification errors. Kovendo's free Confusion Matrix Analyzer provides a convenient way to analyze binary and multiclass confusion matrices and understand important classification metrics. You can read more about Confusion Matrix in machine learning on Kovendo'd blog to become master in that.

To use the analyzer, enter your confusion matrix values and class labels, then run the analysis. The tool can help you examine metrics such as accuracy, precision, recall, F1 score, and class-level performance. This makes it useful for students learning machine learning, researchers evaluating classification experiments, and data scientists analyzing the performance of classification models.

Understanding the Metrics From a Confusion Matrix

A confusion matrix provides the underlying values needed to calculate several important machine learning evaluation metrics. For binary classification, these include true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). These values can then be used to calculate accuracy, precision, recall, and F1 score.

The F1 score combines precision and recall into a single metric using their harmonic mean. If you want to understand the calculation in more detail, see this guide on F1 Score Explained: Formula, Calculation, and Examples . It explains the F1 formula, step-by-step calculations, examples, multiclass F1, and the relationship between F1, precision, and recall.

Accuracy measures the proportion of total predictions that are correct. However, accuracy and F1 score can provide different perspectives, particularly when the classes in a dataset are imbalanced. For a detailed comparison, see Accuracy vs. F1 Score in Machine Learning , which explains when each metric can provide useful information about classification performance.

Precision and recall focus on different types of classification outcomes. Precision measures how many predicted positive cases are actually positive, while recall measures how many actual positive cases the model successfully identifies. You can learn more about their differences and trade-offs in Precision vs. Recall in Machine Learning .

Another important component of a confusion matrix is the true positive. A true positive occurs when a model correctly predicts a positive class. Understanding TP is essential because it is used in the calculation of precision, recall, and F1 score. For a focused explanation, read Understanding True Positives in Machine Learning .

How to Use the Confusion Matrix Analyzer

Enter your class labels and confusion matrix values into the analyzer and select the analysis option. You can use a sample binary or multiclass matrix to understand how the calculations work. The results can then be used to examine overall model performance as well as class-specific behavior.

For binary classification, the analyzer helps you understand the relationship between TP, TN, FP, and FN. For multiclass classification, it can help identify which classes are correctly predicted and which classes are frequently confused with one another.

This information can be particularly useful when accuracy alone does not provide enough detail about a model's behavior. Examining precision, recall, F1 score, and the individual confusion-matrix values provides a more detailed view of classification performance.

Who Can Use This Tool?

• Students learning classification and machine learning evaluation metrics.
• Data scientists analyzing classification-model results.
• Researchers evaluating machine learning experiments.
• ML engineers investigating model errors and class-level performance.
• Developers who need a quick way to calculate and interpret classification metrics.

Whether you need a confusion matrix calculator, confusion matrix analyzer, or an online way to explore accuracy, precision, recall, and F1 score, this tool provides a practical starting point for understanding classification-model performance.