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README.md
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@@ -69,12 +69,10 @@ The primary evaluation metric used is Accuracy. A confusion matrix was also gene
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* **Accuracy:** The proportion of correctly classified images out of the total number of images evaluated.
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\text{Accuracy} = \frac{\text{Number of correct predictions}}{\text{Total number of predictions}}
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* **Confusion Matrix:** A table that visualizes the performance of a classification model. Each row represents the instances in an actual class, while each column represents the instances in a predicted class.
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### Results
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* **Accuracy:** The proportion of correctly classified images out of the total number of images evaluated.
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$$
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\text{Accuracy} = \frac{\text{Number of correct predictions}}{\text{Total number of predictions}}
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$$
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* **Confusion Matrix:** A table that visualizes the performance of a classification model. Each row represents the instances in an actual class, while each column represents the instances in a predicted class.
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### Results
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