The correct answer is **Epoch**.

In machine learning, an "epoch" refers to a single pass through the entire training dataset. During an epoch, the algorithm runs on the full dataset and adjusts the model weights based on the predictions and the actual outcomes. Here's a brief explanation of the other terms for clarity:

1. **Batch**: This typically refers to a smaller subset of the training dataset used to perform a single update to the model's weights.

2. **Learning Rate**: This is a parameter that determines the step size at each iteration while moving toward a minimum of the cost function.

3. **Cost Function**: This is a measure of how well the model's predictions match the true labels of the training data. The goal of training is to minimize this cost function.

In summary, "epoch" signifies how many times the entire training set is used during the training process.

Question

The correct answer is **Epoch**.

In machine learning, an "epoch" refers to a single pass through the entire training dataset. During an epoch, the algorithm runs on the full dataset and adjusts the model weights based on the predictions and the actual outcomes. Here's a brief explanation of the other terms for clarity:

1. **Batch**: This typically refers to a smaller subset of the training dataset used to perform a single update to the model's weights.

2. **Learning Rate**: This is a parameter that determines the step size at each iteration while moving toward a minimum of the cost function.

3. **Cost Function**: This is a measure of how well the model's predictions match the true labels of the training data. The goal of training is to minimize this cost function.

In summary, "epoch" signifies how many times the entire training set is used during the training process.

Knowee AI · Accepted Answer

The correct answer is **Epoch**.

In machine learning, an "epoch" refers to a single pass through the entire training dataset. During an epoch, the algorithm runs on the full dataset and adjusts the model weights based on the predictions and the actual outcomes. Here's a brief explanation of the other terms for clarity:

1. **Batch**: This typically refers to a smaller subset of the training dataset used to perform a single update to the model's weights.

2. **Learning Rate**: This is a parameter that determines the step size at each iteration while moving toward a minimum of the cost function.

3. **Cost Function**: This is a measure of how well the model's predictions match the true labels of the training data. The goal of training is to minimize this cost function.

In summary, "epoch" signifies how many times the entire training set is used during the training process.

____________is the number of times the algorithm runs on the whole training dataset.BatchEpochLearning rateCost function

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