Since the ML process is iterative, is it the same test data used for evaluation? If yes, does repeated exposure to the same test data compromise its usefulness as an unseen dataset?

The process of model development in machine learning is fundamentally iterative, often necessitating repeated cycles of model training, validation, and adjustment to achieve optimal performance. Within this context, the distinction between training, validation, and test datasets plays a major role in ensuring the integrity and generalizability of the resulting models. Addressing the question of whether

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Explain why the network achieves 100% accuracy on the test set, even though its overall accuracy during training was approximately 94%.

The achievement of 100% accuracy on the test set, despite an overall accuracy of approximately 94% during training, can be attributed to several factors. These factors include the nature of the test set, the complexity of the network, and the presence of overfitting. Firstly, the test set may differ in various aspects from the training

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How is the training data split into training and test sets in TensorFlow.js?

In TensorFlow.js, the process of splitting the training data into training and test sets is a important step in building a neural network for classification tasks. This division allows us to evaluate the performance of the model on unseen data and assess its generalization capabilities. In this answer, we will consider the details of how

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