Locating Weaknesses in Machine Learning Models

Locating Weaknesses in Machine Learning Model

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 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. 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 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.