I want to go through the Wikipedia series on Machine Learning and Data mining. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
Batch learning, also termed offline learning, is that type of learning where the model undergoes a training process from the entire batch of data. Normally, it involves feeding of what is referred to as batch data, which includes inputting all available data at once into the learning algorithm; a process which results in the creation of a model that can be used to make the prediction. Once trained, the model is not updated by default; the only way to rebuild a given model is restructuring it with new data.
Data Processing: Trained on the entire dataset and focused on deep learning algorithms
Model Update: Parameters in a model are updated rarely, and earlier they need to be trained again with the entire dataset
Resource-Intensive: Extremely computationally and memory-intensive, where large amount of data is crunched.
Predictive Performance: In some cases, it may achieve very high accuracy because of detailed analysis of the data used in the training phase
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