Difference Between Data Classification and Prediction in AI


In machine learning, it is important to understand the difference between data classification and data prediction (or regression) and apply the right concept when a task arises. This is because classification involves a premature decision (as in a combination of prediction and past decisions) and the decision-maker may end up making a decision based on incorrect elements. Not a good situation when error-free action is the main aim of using such a model.

As artificial intelligence and associated technologies evolve, for a program to make the right decision actively depends on how a particular task compares with a set of reference data. This set of data, in most cases, is crucial in the development of that system.

So, let’s take a closer look at these two separate concepts and then go through some of their core differences in the context of decision-making in AI and other related fields.

What is Data Classification?

In simple words, classification is a technique where a system determines what class or category a given observation falls in so that the future course of action can then be defined. Machine learning training uses a three-pronged approach to do this:

  • Application of a classification algorithm that identifies shared characteristics of certain classes
  • Comparison of this set of characteristics with that of the given observation
  • The conclusion to classify the given observation

Let’s take the example of a real-life event to better understand this.

If a bank wants to use its AI tool to predict if a person will default on his loan repayments or not, it will need to first classify the person. For a bank, the two classes in this context will be defaulters and non-defaulters. It does not care about any more details.

The tool will execute the above-mentioned three-step process to conclude if the person will fall in the first or the second category.

While this process may seem unbiased, there is one major drawback that data classification has. This is called the black box problem. It involves a lack of identification of the specific characteristic that must have influenced the decision to classify a given observation under one class. It can be one or more characteristics but pinpointing which ones will be impossible.

What this means is that the bank cannot use it as an example to deter other applications. It has to run the tool every time to assess the person. And for all persons, this defining characteristic may differ.

How Does Data Prediction Compare?

If data classification deals with determination based on characteristics, data prediction focuses on coming up with a more polished output (e.g.: a numeric value). Such type of regression analysis is often used for numerical prediction.

In the above example of the bank loan defaulter, a data prediction model will come up with the probability of how likely a person is to default on loan repayments rather than mere classification.

What is the Difference?

As one can observe, there is a stark difference between data classification and data prediction. Although both of them are widely used in data analysis and artificial intelligence tools, they often serve separate purposes.

According to Frank Harrell, a professor of biostatistics at Vanderbilt University, classification is a forced choice. He takes the example of the decision that a marketer has to take when she has to plan how many of the total target audience should she focus on when building a marketing plan on, say, Facebook. Here, “a lift curve is used, whereby potential customers are sorted in decreasing order of estimated probability of purchasing a product.” To gain maximum ROI, the marketer targets those that are likely to buy the product. Here, classification is not required.

When to make a forced-choice and when not to totally depends on the observation being made. This is why most algorithmic models working on data analysis cannot be used for all types of results. Classification and prediction both depend on what the required output is.

Now, let’s take a look at some pointers that will further clarify the differences between these two models:

  • In classification, a data group is divided into categories based on characteristics
    • In prediction, the reference dataset is used to predict a missing element
  • Classification can be used when the type of desired output is already known
    • Prediction, on the other hand, is used when the type is unknown
  • Classifiers are dependent on previous data sets. They require abundant information to provide better prediction
    • Numerical regression, on the other hand, can provide usable data and act as a starting point for future activities

What these differences highlight is the need to apply them cautiously. Choosing one method over another might feel like a free option, but it is much more than that. While preparing the data will involve challenges in relevance analysis and data cleaning (to chuck out as much noise as possible), one has to also consider factors such as accuracy, speed, robustness, scalability, and interpretability.

As one can assume, these two models have differing values as far as the above factors are considered. The computational cost, for example, is not an important topic at the moment in the artificial intelligence field. But once these models become a part of the everyday analysis, a discussion will surely pop up. And that’s when a more learned decision must be taken.

Finally, it is important to understand that both classification and regression (prediction of a numerical value) are types of predictive analysis. The difference is mainly in how they interact with observation as well as how the reference data set is used.

Choosing which one to go with should perfectly fit the case otherwise one will end up with the wrong choice. Building such predictive models should, therefore, be a joint project involving data scientists and business users. In the example of the bank, if loan agents can directly work with data scientists during the development of these models, it aids in removing at least the known errors out of the equation.

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