With the revolution of data, almost every organisations are shifting towards making data-driven decisions. During the last few decades, machine learning has been playing as a protagonist in a number of domains in the organisations. It has been broadly used in domains like speech recognition, autonomous vehicles, medical imaging, etc. With the researches going on in both academia and organisations, machine learning has developed in an exponential way within a shorter span of time.
In this article, we will discuss the model-based machine learning technique and how it helps to overcome the challenges of traditional machine learning.
Traditional Machine Learning
Algorithms such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, K Nearest Neighbours, Neural Networks, and various other algorithms constitute machine learning methodology. In order to solve a specific machine learning problem, one must select a suitable algorithm to implement on existing software or may need to write their own implementation. This method has been successfully applied in many problems in the organisations and has proved to be very efficient. However, the traditional approach of machine learning has some notable limitations.
Challenges In Traditional Machine Learning
The challenges can be described as below
- The difficulty of adapting a standard algorithm to match the particular requirements of a specific application.
- some problems can be tackled using off-the-shelf machine learning methods, others will require appropriate modifications, which in turn requires an understanding both of the underlying algorithms and of the software implementation
- there are many applications for which it is difficult to cast a solution in the form of a standard machine learning algorithm. Eg. Bayesian Ranking problem
Model-Based Machine Learning (MBML)
The model-based Machine Learning methodology was introduced by a Microsoft researcher, Chris Bishop. The main goal of model-based machine learning is described as “a single framework which supports a wide range of models”. It is focused on a powerful framework based on Bayesian inference in probabilistic graphical models.
The key goals of a model-based approach include the following:
- The ability to create a very broad range of models, along with suitable inference or learning algorithms, in which many traditional machine learning techniques appear as special cases.
- Each specific model can be tuned to the individual requirements of the particular application: for example, if the application requires a combination of clustering and classification in the context of time-series data, it is not necessary to mash together traditional algorithms for each of these elements (Gaussian mixtures, neural networks and hidden Markov models (HMMs), for instance), but instead a single, integrated model capturing the desired behaviour can be constructed.
- Segregation between the model and the inference algorithm: if changes are made to the model, the corresponding modified inference software is created automatically
- Transparency of functionality: the model is described by compact code within a generic modelling language, and so the structure of the model is readily apparent.
- Pedagogy: Newcomers to the field of machine learning have only to learn a single modelling environment in order to be able to access a wide range of modelling solutions
Benefits of MBML
To overcome these challenges of traditional machine learning, the new-paradigm, MBML is introduced in order to make the process more transparent. The advantages of this methodology are mentioned below
- This methodology has an auto-generated inference algorithm
- It has an easy extension to more complex situations, a model can be modified in a flexible manner using the same inference algorithms
- The coding is compact, it is easy to write and maintain with a transparent functionality.
- Unlike traditional machine learning, it has only one simple framework.