Locating Weaknesses in Machine Learning Models

Locating Weaknesses in Machine Learning Model

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 . 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. 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 , Google, and Facebook have also employed 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.