One of the most important and hot topics in Machine Learning nowadays is Occam’s razor principle. Does it sound unclear to you? Do not worry at all! Imarticus’s AIML program offers various Machine Learning courses which provide the basic study and understanding of the Occam’s Razor principle. Stay tuned to this article to kickstart your Machine Learning career.
Categories of Machine Learning algorithms
The Machine Learning algorithms have mainly two different categories: supervised and unsupervised. When we talk about supervised learning, the model is trained with the labelled data taken from the previous sets for future predictions. On the other hand, with unsupervised learning, the process is applied exclusively to unlabeled data only. This is mainly used to identify well the patterns and structures in the data sets that were unexplored and unknown (sometimes referred to as ‘discovery analysis’.
Occam’s Razor Principle: What does it mean?
In simple words, Occam’s Razor advises using simple ML-based models and algorithms with fewer coefficients as compared to the complex ones (Eg. ensembles). The use of Occam’s Razor can be traced back to the 1200s by William of Ockham, who suggested using the simplest, efficient and most direct solution with the least number of assumptions and variables. There are certain applications and considerations to make based on Occam’s Razor as enlisted below:
Choosing the right model
Selecting the model from different available ML models to create a predictive project is termed model selection. Usually, a model is selected based on its performance like low prediction error and high accuracy. One should also consider the fact that a simple model should be preferred over complex ones as they have fewer coefficients during evaluation.
Simplifying the model
Dimensionality reduction and feature selection are some of the simplification procedures which make use of Occam’s Razor. This results in improved results with less investment of time and energy.
Modern state of art applications
One of the most useful applications of Occam’s Razor principle is in the state of art technologies, especially the direct application to Machine Learning. The programmers and engineers work collectively to train computers with data sets and extend their limitations of the already existing codebase data structure programming. This allows the computer systems to produce astonishing and favourable results in no time.
Some various other applications of Occam’s Razor principle is the setting of the parameters for specific Machine Learning concepts like Bayesian Logic. The programmers make use of this principle to make the model simpler and highly efficient. One of the important things to take care of is the correct application of Occam’s Razor. Incorrect usage and application can decrease the efficiency and credibility of Machine Learning programming. Interestingly Albert Einstein was Occam’s greatest disciple who said “Everything should be made as simple as possible, but not simpler”.
If you want to start any project based on Machine Learning, it should always address the essential business question and problem that you intend to resolve. With the assumptions of other criteria remaining the same, Occam’s Razor can be applied successfully to chose a model which is simple to implement, interpret, understand, explain and maintain in the long run. In simpler words, choose the model that gives accurate results using this principle. The main idea lies in examining the project scope to a deep level, analysing the inputs, data sets and parameters to get the desired outcomes. A proper and well-defined machine learning training can result in a better understanding and implementation of the Occam’s Razor principle in solving real-life problems and deal with challenges.