Articles → MACHINE LEARNING → Classification Report In Machine LearningClassification Report In Machine LearningThis article describes the classification report in Machine Learning.Purpose A classification report is a performance evaluation metric that gives a detailed breakdown of how well a classification model is performing.Components Of A Classification Report The classification report has the following components: -PrecisionRecallF1-ScoreSupportLet us discuss each component.Precision Precision specifies, out of all the positive predictions that the model has made, how many of them are correct?The formula is: -ParameterDescriptionTP (True Positive)The model correctly predicted positive.FP (False Positive)The model incorrectly predicted positive.High precision is equivalent to a low number of false positives. For example, if your model predicts cancer for 100 patients, but only 80 have cancer. The precision is 80%.Recall Actual PositiveActual Positive means the number of cases in your dataset that truly belong to the positive class, regardless of what the model predicts.Recall measures how well a model finds all the actual positives in the dataset.The formula is: -ParameterDescriptionFN (False Negative)Actual positives that the model missedConsider a scenario where we build a model to detect whether a patient has cancer. Actual positives (patients with cancer) = 100 Model Predicts: 80 correctly detected (TP = 80) 20 missed (FN = 20) So, the model caught 80% of cancer cases.F1-Score Support It simply counts how many samples belong to each class, independent of predictions.Posted By - Karan Gupta Posted On - Friday, October 17, 2025 Query/Feedback Your Email Id** Subject* Query/Feedback Characters remaining 250**
Actual Positive means the number of cases in your dataset that truly belong to the positive class, regardless of what the model predicts.
Actual positives (patients with cancer) = 100 Model Predicts: 80 correctly detected (TP = 80) 20 missed (FN = 20)
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