batchbasert

Batch-based learning, often referred to as batch learning or batch training, is a fundamental approach in machine learning where the model is trained using a fixed dataset, known as a batch. In this method, the entire dataset is passed through the model, and the weights are updated after processing the entire batch. This contrasts with online learning or incremental learning, where the model is updated incrementally with each training example.

The primary advantage of batch-based learning is its simplicity and the ability to leverage vectorized operations, which can significantly speed up training on modern hardware. However, it requires sufficient memory to store the entire dataset, which can be a limitation for very large datasets. Additionally, batch-based learning can be less efficient in terms of computational resources compared to online learning, as it does not update the model until the entire batch has been processed.

Batch-based learning is commonly used in scenarios where the dataset is relatively small and can fit into memory, or where the dataset is static and does not change frequently. It is the basis for many traditional machine learning algorithms, such as linear regression, logistic regression, and support vector machines.

Variations of batch-based learning include mini-batch learning, where the dataset is divided into smaller subsets, and stochastic gradient descent (SGD), where the model is updated after processing each individual training example. These variations aim to balance the trade-offs between batch-based learning and online learning, offering a compromise in terms of computational efficiency and memory usage.