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Question 1 of 29
In a machine learning project focused on image recognition, a team is tasked with developing a model that can accurately classify images of animals. They decide to implement a convolutional neural network (CNN) architecture. After training the model on a dataset of 10,000 labeled images, they achieve an accuracy of 85% on the training set. However, when they test the model on a separate validation set of 2,000 images, the accuracy drops to 70%. What could be the most likely reason for this discrepancy in performance between the training and validation sets?
The model is overfitting to the training data.
The validation set is too small to provide a reliable estimate of model performance.
The images in the validation set are of lower quality than those in the training set.
The model architecture is too simple to capture the complexity of the data.

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