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Classification usually refers to categorisation of a specific class label that needs to be predicted from the given
input field of data as a result. For example, here we are working on a pure vegetarian service model that predicts
whether the item served is for a pure vegetarian or for all.
Classification Metrics
When evaluating a classification model, it’s important to measure its performance using various metrics. These
metrics help assess how well the model predicts the correct classes. Here are some popular metrics used for
classification models:
• Confusion matrix
• Classification accuracy
• Precision
• Recall
Confusion Matrix
A confusion matrix is a performance evaluation tool used in machine learning to assess the performance of
a classification model. It is a tabular representation that compares the actual labels (true outcomes) with the
predicted labels (model predictions). The table is made with 4 different combinations of predicted and actual
values in the form of 2×2 matrix. The comparison between the prediction and the reality can be used to evaluate
the rate of success. It also gives a clear picture of which classes are being predicted correctly and incorrectly, and
the types of errors are being made.
This matrix is also known as the Error Matrix and is used in situations where we need to evaluate the performance
of the model, identify errors, and find ways to improve the efficiency of the model. It is useful for measuring Recall,
Precision, Accuracy and F1 Score.
The following confusion matrix table illustrates how the 4-classification metrics are calculated (TP, FP, FN, TN), and
how our predicted value is compared to the actual value in the confusion matrix.
Prediction
Confusion Matrix
Yes No
Yes True Positive (TP) False Negative (FN)
Actual
No False Positive (FP) True Negative (TN)
In the Confusion Matrix,
• The target variable has two values: Positive and Negative.
• The columns (Y-axis) represent the actual values of the target variable.
• The rows (X-axis) represent the predicted values of the target variable.
• The numbers in each cell represent the number of predictions made by the machine learning algorithm in each
category
To understand the confusion matrix, let’s understand the following terms:
• Positive: The prediction is positive for the scenario. For example, it will rain today.
• Negative: The prediction is negative for the scenario. For example, it will not rain today.
• True Positive: The predicted value matches the actual value i.e.; the actual value was positive and the model
predicted a positive value.
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