Differences Between Machine Learning and Predictive Modelling
With the continuous stream of big data flowing into the system with every passing day, it becomes important for systems to manage data and apply smart algorithms. The world is becoming a better place by use of various Predictive, learning and statistical tools which are helping businesses in providing prediction and in making it become an overnight success. They also help in shaping the technology trend in a spectacular way.
Two such techniques are machine learning vs predictive modeling that are the buzzwords of today.
Machine learning is an area of computer science which uses cognitive learning methods to program their systems without the need of being explicitly programmed. In other words, those machines are well known to grow better with experience.
Machine learning is related to other mathematical techniques and also with data mining which encompasses terms such as supervised and unsupervised learning.
Predictive modeling, on the other hand, is a mathematical technique which uses statistics for prediction. It aims to work upon the provided information to reach an end conclusion after an event has been triggered.
In a nutshell, when it comes to data analytics, machine learning is a methodology which is used to devise and generate complex algorithms and models which lend themselves to a prediction. This is popularly known as predictive analysis in commercial use which is used by researchers, engineers, data scientists and other analysts to make decisions and provide results and uncover the hidden insights by making use of historical learning.
In this post, we are going to study in detail about the differences.
Head To Head Comparison Between Machine Learning vs Predictive Modelling (Infographics)
Below is the Top 8 Comparison between the Machine Learning vs Predictive Modelling
Key differences between Machine Learning vs Predictive Modelling
- Machine learning is an AI technique where the algorithms are given data and are asked to process without a predetermined set of rules and regulations whereas Predictive analysis is the analysis of historical data as well as existing external data to find patterns and behaviors.
- Machine learning algorithms are trained to learn from their past mistakes to improve future performance whereas predictive makes informed predictions based upon historical data about future events only
- Machine learning is a new generation technology which works on better algorithms and massive amounts of data whereas predictive analysis is the study and not a particular technology which existed long before Machine learning came into existence. Alan Turing had already made used of this technique to decode the messages during world war II.
- Related practices and learning techniques for machine learning includes Supervised and unsupervised learning while for predictive analysis it is Descriptive analysis, Diagnostic analysis, Predictive analysis, Prescriptive analysis, etc.
- Once our machine learning model is trained and tested for a relatively smaller dataset, then the same method can be applied to hidden data. The data effectively need not be biased as it would result in bad decision making. In the case of predictive analysis, data is useful when it is complete, accurate and substantial. Data quality needs to be taken care of when data is ingested initially. Organizations use this to predict forecasts, consumer behaviors and make rational decisions based on their findings. A success case will surely result in boosting business and firm’s revenues.
Machine Learning vs Predictive Modelling Comparison Table
Basis for Comparison
|Definition||Method used to devise complex algorithms and models that lend themselves to prediction. This is the core principle behind predictive modeling||An advanced form of basic descriptive analytics which makes use of the current and historical set of data to provide an outcome. This can be said to be the subset and an application of machine learning.|
|Modus operandi||Adaptive technique where the systems are smart enough to adapt and learn as and when a new set of data is added, without the need of being directly programmed. Previous calculations will be used to provide effective results||Models are known to make use of classifiers and detection theory to guess the probability of an outcome given a set of input data|
|Approaches and models||
|Update Handling||Statistical model is updated automatically||Data scientists need to run the model manually multiple times|
|Requirement clarification||Proper set of requirements and business justifications need to be provided||Proper set of business justifications and requirements need to be clarified|
|Driving technology||Machine learning is data driven||Predictive modeling is use case driven|
Conclusion – Machine Learning vs Predictive Modelling
Both these technologies are providing solutions to organizations worldwide in their own realms. Top organizations like Google, Amazon, IBM, etc. are investing heavily in these artificial intelligence and machine learning algorithms to tackle real-world problems in a better and an efficient manner. It is up to you to decide what kind of method your business need. Go ahead write to us in the comment section below which technology benefited you in what way.
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This has been a guide to Machine Learning vs Predictive Modelling, their Meaning, Head to Head Comparison, Key Differences, Comparision Table, and Conclusion. You may also look at the following articles to learn more –