Introduction to Batch and Online Learning
Our series on Machine Learning has elaborated on a variety of topics related to the subject. Firstly, we began by providing an overview to the various machine learning systems that appear in data science, as well as the algorithms associated with these systems. You can explore this overview, which also introduces batch and online learning topics, here. We then sought to dive deeper into these individual systems, beginning first with a thorough discussion of supervised machine learning and its cohort of clustering and regression algorithms. This was logically followed, in similar fashion, by discussion on the features of unsupervised machine learning. We follow these elaborations with an analysis of batch and online learning systems. This is the focus of the present article. Let’s begin.
What is Batch Learning?
As we move on from supervised and unsupervised learning, batch learning provides us with an avenue through which we may answer the second question. This question is whether or not the machine learning system can learn on the fly or incrementally. With regards to these fundamental premises, batch learning is of particular import as these systems learn on the fly. Batch learning systems are incapable of learning incrementally as they must be trained with all of the data at once. The system trains momentarily and subsequently launches. The system then applies what it has learned with new data. We may also understand this feature as offline learning. Furthermore, teaching the system with new data requires creation of a new version of the system.
Putting Batch Learning in Practice
When putting Batch Learning into practice, there are two types of training mechanisms we can implement. These include full batch learning and mini batch learning. In full batch learning, the entirety of training data enters into the machine learning program. In this manner, each data object can be taken into account in conjunction with each other to provide an accurate model for predicting non-training data. Alternatively, mini-batch learning occurs through a turnover of training data objects and creates an estimation of the model and uses this estimation to predict non-training data object.
Full batch learning is a less random batch learning mechanism. When it comes to executing this type of machine learning system, one of the essential components is identifying a viable batch size for the machine learning model. With full-batch learning, if the size of the training data set harbors 1000 data items, then the batch size for this learning model also harbors 1000. Alternatively, in mini-batch learning, the batches might be 100 data objects, or 200, etc.
Batch Reinforcement Learning
Batch learning can actually apply to a type of learning known as reinforcement learning. With reinforcement learning, an agent within the program is capable of observing the data-construted environment and perform actions. These actions generally return rewards which either instruct an agent to repeat the action or cease the action.
Adding batch learning to reinforcement learning solves a particular problem of the latter consequence of the fact that teaching an agent can take too much time if the system is overly complex. By providing the agent with large aggregates of data, such an issue may be solved.
What is Online Learning
Along this thread of whether or not a system is capable of learning incrementally, online learning permits this exceptionally. In online learning, a learning system uses mini-batches of data sequentially such that the system can constantly improve its methodology. One of the advantages of this regimen is the fact that it is quite quick. Furthermore, it does not require a large initial training set. Thus, the benefit of this technique is that the model is capable of adapting to new data in the model as it arrives.
Application of Online Learning
Firstly, it is important to note that online machine learning models carry a minority of the workload. Much of machine learning executes the model utilizing batches. Furthermore, online learning can be a rather tricky endeavor with a myriad of pitfalls, but if you get it right, it can be extremely powerful. One of the primary uses of online learning manifests through its dynamic properties. In particular, its dynamism derives from its ability to receive and manage a constant flow of information. For this reason, the machine learning model must be adept in adapting to a constantly changing data environment in order to derive a proper solution.
It is also particularly applicable for instances wherein there is insufficient memory to store training data. In this case, when the data content is beyond the storing capabilities of the computer memory, online learning can be employed to teach the model incrementally so that the data does not take up such large amounts of space.
Specifying the Learning Rate
When working with an online learning model, it is important to establish the learning rate of the system. According to Jason Brownlee in his comprehensive article at Machine Learning Mastery, he describes the learning rate of a machine learning system as a quasi thermostat such that the rate of model change in response to error can be controlled. Establishing a rapid learning rate facilitates rapid adaptation, allowing the system to quickly account for changes as new data enters the system. Alternatively, low learning rates exhibit longer durations of adaptation bur are unlikely to forget various parameters of the data. Which one you employ depends on the circumstances of the data and environment you operate in.
The Take Away
The differences between batch-based and online machine learning provide different avenues for creating the machine learning model. The batch form of machine learning relies on establishing a size of the training data and incoming data. Once the program is trained, it is launched, but cannot respond to changes in the data unless it is trained again. Alternatively, the online machine learning model is capable of responding to such changes as it learns progressively over time. Though a minority of machine learning models, it can be very powerful as it improves over time. The method you choose depends entirely on the purpose of the model itself. Hopefully this tutorial has proven helpful for individuals just now getting in to machine learning. Our subsequent article will elaborate on the implementation of instance-based and model based machine learning.