First Images are loaded from the specified folders, converted to grayscale, resized, and normalized.
Second Model training : Random Forest classifier with 100 trees is trained using the flattened image arrays from the training set.
Next we have Model evaluation: The trained classifier is evaluated on the test set using accuracy, recall, and F1 score metrics. Additionally, a confusion matrix is generated to visualize the classifier's performance.
First Images are loaded from the specified folders, converted to grayscale, resized, and normalized. Second Model training : Random Forest classifier with 100 trees is trained using the flattened image arrays from the training set. Next we have Model evaluation: The trained classifier is evaluated on the test set using accuracy, recall, and F1 score metrics. Additionally, a confusion matrix is generated to visualize the classifier's performance.