Locating Weaknesses in Machine Learning Models

What is Machine Learning?

As the name is self-explanatory, machine learning refers to the phenomenon of machines being able to carry out programs without any human intervention. Though popularized extensively in the 21st century, it is not a recent phenomenon and has been worked upon since the 1940s. The term was coined by Arthur Samuel in 1952, during his efforts to design a computer program for playing checkers. He identified that repetitive playing of the game by the program increased the efficiency of the program to come up with moves for winning strategies. In similar ways, data is fed to the machine, and using the same data machines are trained using various algorithms to come up with models for personalized applications. In its most recent application, machine learning was used by researchers to use blood tests for the prediction of survival chances that a patient has upon contraction of the coronavirus. 

Difference between Artificial Intelligence and Machine Learning? 

On the cursory reading, both artificial intelligence and machine learning might appear to be doing the same job, but they have their set of differences. Artificial intelligence is a wider phenomenon, an umbrella term that aims to create models with human intelligence, whereas machine learning is a subset of artificial intelligence that has limited functioning to the data set which is provided to it. Artificial intelligence focuses to gain a critical thinking ability, and rationality to conduct operations on a large scale as a general phenomenon, and machine learning remains limited to the specific problem. 

What is a Machine Learning Model? 

In a literal sense, machine learning models are described as the ‘mathematical engines’ of artificial intelligence that are provided with data sets to find the patterns of how things function and make accurate predictions using the same. The data scientists contribute to the training of the model by providing the required algorithm to learn from the data. The data used in the first place is referred to as the ‘target attribute’. The main aim of the algorithm is to identify the patterns when the data is put to use and chart out the recurring functions. Once this is done, an output is produced by the model for future predictions. For example, an ML model can be trained to detect whether an email is a legitimate one or spam. 

How to build a Machine Learning Model? 

The contemporary times require a high demand for ML models. The first step is to locate the problem, what business needs to be targeted, and a proper diagnosis of the objectives that need to be fixed. This is the fundamental step in the building of the model. Secondly, the data needs to be identified, how much of it is required and where it comes from that is the source. This proves as an incentive for a better model. The collected data then, needs to be scrutinized, standardized and duplicity of information has to be eliminated. The data scientists then chose the required technique and algorithm for maximum optimization. This is a crucial step for the model as it determines its efficiency in dealing with real-world cases. A constant trial of the model for continuous evaluation is needed. This is referred to as the ‘operationalization’ of the model where benchmarks for improving the overall performance are laid down. The model needs to be updated from time to time according to the needs and preferences of the business. 

What are the different Machine Learning Models? 

There are various debates on the exact number of various types of ML models, and while there is no agreement to the specific types, research has classified 4 types of models for a wider agreement and understanding. 

Supervised Learning Model 

The main aim of this model is to supervise the predicted data set. In this model, the algorithm learns from the dataset which has already included the output. The machines are trained using the ‘labeled dataset’ in which some inputs correspond to the output. The supervision comes into play once the desired prediction has been created. The learning algorithm is updated until satisfactory results are achieved. In our everyday lives, this model functions in determining spam filtration, risk assessment, and fraud detection. It is further classified into classification and regression algorithms. 

Unsupervised Learning Model 

In this model, the machine is trained using an ‘unlabeled dataset’, and unlike the supervised model, there is no mechanism of supervision. Its main aim is to determine the categorization of the unsorted datasets according to different characteristics. The highlight is to determine the hidden patterns of similarities and differences within the dataset. It focuses on the creation of a mapping function to classify data on features based on the data. It is further categorized into clustering and association algorithms. 

Reinforcement Learning Model 

In this model, the machine learns from its own set of experiences and there is no labeled data presented to it. The model is quite similar to human learning, in which the agent explores its surroundings, and gets rewarded at the end for the actions. This model aims to maximize the rewards. It employs the ‘Markov decision process’ where the agent while interacting with the environment responds and generates a new state. This model is used in the fields of game theory, information theory, and multi-agent systems. It is further graded into positive and negative learning algorithms. 

Semi-supervised Learning Model 

This model employs a combination of both labeled data as well as unlabeled data. The labeled data enables the model a partial training of the algorithm required, and the unlabeled data enables pseudo-labeling. The unlabeled data is in greater quantity in this model. It is mainly used in applications such as speech analysis, web content classification, and text document classifier. 

Locating the weaknesses in Machine Learning Models 

Despite the vast popularity and easy handling of problems, there are issues with the models of machine learning that need to be identified and to be handled properly. Different sets of models that are discussed above have loopholes that data scientists are constantly working with to make sure it is not repetitive. 

Detecting the issue of Over-fitting 

This is one of the most common issues associated with machine learning. It occurs when there is a massive amount of biased data in the training dataset. This creates the issue of negative probability, as the model captures noise and inaccurate data. The reason behind the occurrence of this issue is using non-linear methods which contribute to the building of non-realistic data models. This issue can be solved using parametric and linear algorithms. 

Recognizing the problem of Underfitting 

This is another weakness of the ML model. It occurs when training is imparted to the machine with a very minimal amount of data. This results in a breakdown of the functioning when the machine encounters complex problems and leads to wrong predictions as well. It can be overcome by increasing the number of epochs, and model complexity. 

Identifying the Non-representative Training Data 

In many cases, there are restricted datasets provided, that fail to cover the cases that have already occurred as well as those cases which are occurring. In situations like these, the machine is exposed to a ‘sampling noise’ in which there is biased data for a certain class or group. This leads to inaccurate predictions and fewer generalizations. 

Recognizing inadequate Training Data 

This is a major weakness of the machine learning models which is impacted by both the quantity as well as the quality of the data. The algorithms require processing large amounts of data and for their ideal functioning, quality plays an essential role. Many factors impact the models such as ‘noisy data’, ‘incorrect data’, and ‘generalizing of output data’. These create a deficiency in giving accurate prediction, classification, and the accuracy of results. This in turn leads to faulty programming and poor actions in the future. 

The problem of Data-drift 

This occurs when the model keeps showing earlier recommendations and is not aware of the changes in the data. It can be overcome by regular monitoring. 

Conclusion

The world is transforming rapidly and humans need to catch the pace with the introduction of new technologies and systems. Machine Learning models despite their weaknesses have brought solutions to complex problems and continue to evolve with mechanisms that will transform the way the world functions. It is a promising domain for various fields and provides innovation not just in various institutions but also compensates for high return technology. Its functioning can be seen in major industries such as healthcare, banking, marketing, infrastructure, etc. The big giants such as Amazon, Google, and Facebook have also employed machine learning to create a space that leads to the benefit of the population at a large scale.  

We hope that this article was able to provide you with answers about the fundamentals of Machine Learning, and how constant effort needs to be put into making its functionality more efficient and at the same time removing barriers to enhance user’s experiences. Thank you for showing interest in our blog and if you have any queries/suggestions/feedback/comments, you can write to us at info@futureanalytica.com.

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