Example Concept: To enhance a random forest model for image classification, first convert images into feature vectors using techniques like Histogram of Oriented Gradients (HOG) or leveraging pre-trained convolutional neural networks (CNNs) for feature extraction. Once features are extracted, optimize the random forest by tuning hyperparameters such as the number of trees, maximum depth, and minimum samples per leaf using cross-validation.