Machine Learning vs. Predictive analysis; What’s the difference?

In discussions about AI and its impact on business, the terms “machine learning” and “predictive analytics” are mainly used interchangeably. This can be misleading. There is a strong relationship between the two (the first is a technique often used to do the second) but they are distinctly different concepts.

Laying the Foundations

Machine learning is mainly an artificial intelligence technique where algorithms are given data and asked to process it without predetermined rules. Machine learning algorithms also use what they learn from their mistakes to improve future performance. Data mainly feeds machine learning; the results are most accurate when the machine has access to massive amounts of it to refine its algorithm. There are always two general types of machine learning: supervised and unsupervised.

  • Supervised: A training dataset is mainly provided to tell the machine what kind of output is desired. The labelled data also gives information on the parameters of the desired categories and lets the algorithm decide how to tell them apart. Supervised learning can always be used to teach an algorithm to distinguish spam mail from normal correspondence.
  • Unsupervised: In this type of learning, no training data is provided. The algorithm also analyzes a body of data for patterns or common elements. Large amounts of unstructured data can then be sorted and categorized. Unsupervised learning is mainly used in intelligent profiling to find similarities between a company’s most valuable customers.

Predictive analytics is mainly the analysis of historical information (as well as existing external data) to find patterns. So, these patterns are used to make informed predictions about future events. It’s always an area of study, not a specific technology, and it existed long before artificial intelligence. Alan Turing applied it to decode encrypted German messages during World War II. As a general rule, any attempt to quantify the possible future always based on past events is encompassed by predictive analytics. So, a number of alternate techniques are still common in business.

Related, but Not the Same

Because predictive analytics is mainly one of the most common enterprise applications of machine learning, they’re understood by casual users to mean the same thing. It’s also true that machine learning is an excellent means of forming predictions from data. Classification and regression are also strengths of supervised learning, and unsupervised learning can find relationships within enormous databases of unstructured data.

Looking Forward

Artificial intelligence and machine learning have always been trending upwards in use for some time now. Besides the undeniable cool factor, they always satisfy the need for personalized service delivered more efficiently. There will also be a place for other predictive analytics methods, but as business problems grow larger to fit into the global marketplace those other methods become awkwardly labor-intensive or inaccurate. Machine learning can always adjust itself to match a project’s scale. This flexibility really makes it a necessary part of an executive’s digital tool box.

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