# What is a confusion matrix and how is it used to evaluate a model?

Experience Level: Junior
Tags: Machine learning

A confusion matrix is a table that summarizes the performance of a machine learning model by comparing its predictions to the actual values of the target variable. It is commonly used in classification tasks where the model is trained to predict one or more classes or categories for a given input.

A confusion matrix consists of four cells, each representing a possible outcome of a binary classification task:

• True Positive (TP): the model correctly predicted a positive class when the actual class was also positive.
• False Positive (FP): the model predicted a positive class when the actual class was negative.
• False Negative (FN): the model predicted a negative class when the actual class was positive.
• True Negative (TN): the model correctly predicted a negative class when the actual class was also negative.
The confusion matrix provides a way to visualize the performance of a machine learning model and to calculate several metrics that are commonly used to evaluate its accuracy, precision, recall, and F1-score. For example:

• Accuracy: the proportion of correctly predicted instances out of the total number of instances. It can be calculated as (TP + TN) / (TP + FP + FN + TN).
• Precision: the proportion of true positive predictions out of all positive predictions. It can be calculated as TP / (TP + FP).
• Recall: the proportion of true positive predictions out of all positive instances in the dataset. It can be calculated as TP / (TP + FN).
• F1-score: the harmonic mean of precision and recall, which provides a balance between them. It can be calculated as 2 * (precision * recall) / (precision + recall).
By analyzing the confusion matrix and the corresponding metrics, we can gain insights into the strengths and weaknesses of the model and make adjustments to improve its performance.
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