We have all been through that experience of having to deal with misfit uniforms, haven’t we? How does it feel? Uncomfortable, and at times, embarrassing isn’t it! Yes, that’s exactly what trained data science engineers feel when their machine algorithms are labeled as overfit or underfit models, and often trashed for the lack of regularisation. Most machine learning courses have a topic on “what is regularisation in machine learning?” and there are countless tutorials on how to handle regularisation for various purposes. Yet, this remains one of the most complex challenges for a majority of data science teams.
Well, your experience with a 4-wheeled vehicle will come in handy in understanding what is regularisation in machine learning and why it is such a critical issue in data analytics.
Poorly aligned wheels not only deliver poor experience but also put your car and life in grave danger. Moreover, your axle will or gearbox or suspension would be damaged beyond repair if maintenance is not done in time. These are called noise and vibrations. Similarly, overlooking regularisation techniques would induce “noise” in your machine learning data techniques leading to risks of overfitting. The tragedy of working with regularisation in machine learning is that it discourages or stops a machine learning model from learning new techniques on its own just to avoid overfitting or underfitting in any way.
Now, in this article, we have already explained what is overfitting and how L1 and L2 regularisation actually works in a mathematical formulation.
Now, let’s understand advanced techniques of regularisation in machine learning.
We would call these regularisation elements for multi-tasking learning, based on integrated machine learning models.
Finding the best landing pad for Regularisation in machine learning
I presumably favor the cognitive bias technique in regularisation for machine learning models.
There are many different types of cognitive biases that you would have to analyze and rank for your machine learning project. These are mostly from the family of Self-serving bias, hindsight bias, anchoring, and so on.
Now, what you should be focused on during ML solving techniques with regularisation pertains to – Algorithmic bias, which refers to a set of biases / noises / errors resulting from systematic and unfair treatment of different data types and manipulation of outcomes.
From modern AI ML software to credit scoring software, everything you see in the business world has the potential to adapt to regularisation, provided it maintains “algorithmic neutrality.”
In the recent years, we have seen a massive splurge in the machine learning innovations related to multi-task learning, or MTL. MTL is a fast-growing subdomain with machine learning that is found to be the precursor to all advanced artificial neural networking, GANs, and cognitive learning algorithms. In its current context, the use of regularisation for MTLs is found to be extremely important to the generalization of domain information pertaining to signals associated with “inductive transfer” and “inductive biases.”
What is an inductive transfer?
Also referred to as ‘transfer learning’, Inductive transfer is a research problem associated with the advanced ML algorithms used to solve mutually dependent problems. For example, machine learning software used in facial recognition and identity management platforms uses TL. The TL, along with regularisation with MTL is used to improve the efficiency of any disparate reinforcement learning agent, thereby providing legitimacy to common problem solving techniques and game theories. Common applications of this TL in MTL with regularisation can be found in cyber security, sales forecasting, and chatbot interactions.
Next, what’s inductive bias?
Common examples of biases in ML are:
- Maximum conditional independence
- Minimum description length
- Nearest neighbors
- Maximum margin / SVMs
- Minimum cross border validation error
- Minimum features, and so on.
In order to understand what is inductive bias, you have to solve mathematical logic, as explained from the point of view of the Bayesian framework, Support vector machines (SVMs), and k-nearest neighbors (KNN). In simple terms, ML engineers dissociate from regularisation and induce noises in the form of biases, so as to gain a sense of predictability to ML outcomes. That is, MTLs are trained to induce and understand bias so that it becomes easy for the model to predict the outcome of any complex problem solving techniques.