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K-Fold Cross-Validation is a technique used in machine learning to assess the performance of a model by dividing the dataset into k subsets and training the model k times, each time using a different subset as the testing set and the remaining data as the training set. This helps in obtaining a more robust performance estimate, especially when the dataset is limited.
In this tutorial, we'll implement K-Fold Cross-Validation for image classification using Python, specifically focusing on a popular deep learning library called TensorFlow and its high-level API Keras.
Make sure you have the following libraries installed:
This code assumes you have a dataset of images (X) and their corresponding labels (y). Make sure to replace the data loading part with your actual data loading code.
The model defined here is a simple convolutional neural network (CNN). Adjust the model architecture based on the requirements of your image classification task.
Feel free to customize the code according to your specific needs and dataset characteristics.
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