The RF Technique

Random Forests is an ensemble learning method that operates by constructing multiple decision trees during training and outputting the mode of the classes (classification) or mean prediction (regression) of the individual trees.

• Functionality: In plant disease detection, RFs aggregate the predictions from various decision trees to classify plant diseases. Each tree is trained on a random subset of the data, and their collective wisdom leads to more accurate and robust predictions.

• Advantages: RFs are less likely to overfit compared to individual decision trees. They are versatile and can handle large datasets with higher dimensionality.