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Machine Learning vs Predictive Modelling

Differences Between Machine Learning and Predictive Modelling

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.

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Head to Head Comparison Between Machine Learning vs Predictive Modelling (Infographics)

Below is the top 8 Comparison between Machine Learning and Predictive Modelling:

Machine Learning vs Predictive Modelling

Key Differences between Machine Learning and Predictive Modelling

Below are the lists of points, describe the key differences between Machine Learning and Predictive Modelling:

  1. 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.
  2. 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
  3. Machine learning is a new generation technology which works on better algorithms and massive amounts of data whereas predictive analysis are 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.
  4. Related practices and learning techniques for machine learning include Supervised and unsupervised learning while for predictive analysis it is Descriptive analysis, Diagnostic analysis, Predictive analysis, Prescriptive analysis, etc.
  5. 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

Following are the list of points that shows the comparison between Machine Learning and Predictive Modelling.

Basis for Comparison

Machine learning

Predictive Modeling
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
  • Decision tree learning
  • Associate rule learning
  • Artificial neural networks
  • Deep learning
  • Inductive logic programming
  •  Support vector machines
  • Clustering
  • Bayesian networks
  • Reinforcement learning
  • Representation learning
  • Similarity and metric learning
  • Sparse dictionary learning
  • Genetic algorithms
  • Rule-based machine learning
  • Learning classifier systems
  • Group method of data handling
  • Naïve Bayes
  • K-nearest neighbor algorithm
  • Majority classifier
  • Support vector machines
  • Boosted trees
  • Random forests
  • CART(Classification and Regression trees)
  • MARS
  • Neural Networks
  • ACE and AVAS
  • Ordinary Least Squares
  • Generalized Linear Models (GLM)
  • Logistic regression
  • Generalized additive models
  • Robust Regression
  • Semiparametric regression
Applications
  • Bioinformatics
  • Brain-machine interfaces
  • Classifying DNA sequences
  • Computational anatomy
  • Computer vision
  • Object recognition
  • Detecting credit card fraud
  • Internet fraud detection
  • Linguistics
  • Marketing
  • Machine perception
  • Medical diagnosis
  • Economics
  • Insurance
  • NLP
  • Optimization and metaheuristic
  • Online advertising
  • Recommendation and search engines
  • Robot locomotives
  • Sequence mining
  • Sentiment analysis
  • Speech and handwriting recognition
  • Financial market analysis
  • Time series forecasting
  • Uplift modeling
  • Archaeology
  • Customer relationship management
  • Auto insurance
  • Healthcare
  • Algorithmic trading
  • Notable features of predictive modeling
  • Limitations on data fitting
  • Marketing campaigns optimization
  • Fraud detection
  • Risk reduction
  • Improved and streamlined operations
  • Customer retention
  • Sales funnel insights
  • Crisis Management
  • Risk mitigation and corrective measures
  • Disaster Management
  • Customer segmentation
  • Churn prevention
  • Financial modeling
  • Market trend and analysis
  • Credit scoring
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
Drawbacks
  • Work with discontinuous loss functions which are hard to differentiate, optimize and incorporate in machine learning algorithms
  • Problem needs to be very descriptive to find the right algorithm in order to apply an ML solution
  • Large data requirements and training data such as deep learning data needs to be created before that algorithm is put to some actual use

 
  • Need for a huge amount of data, as more the historical data, accurate is the outcome
  • Need all past trends and patterns
  • Polling prediction failure takes in view specific set of parameters which are not real time and hence the current scenarios can influence the polling
  • HR analytics is hampered by lack of understanding Human Behavior

Conclusion

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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