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Trending September 2023 # Machine Learning Vs Predictive Modelling # Suggested October 2023 # Top 15 Popular

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Differences Between Machine Learning and Predictive Modelling

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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 Machine Learning and 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:

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 that works on better algorithms and massive amounts of data, whereas predictive analysis is the study and not a particular technology that 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 include 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 and consumer behaviors and make rational decisions based on their findings. A successful case will surely result in boosting business and the firm’s revenues.

Machine Learning vs Predictive Modelling Comparison Table

Following is the list of points that show the comparison between Machine Learning and Predictive Modelling.

Basis for Comparison

Predictive Modeling

Definition The method used to devise complex algorithms and models that lend themselves to prediction. This is the core principle behind predictive modeling.

Modus Operandi An adaptive technique is 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


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)


Neural Networks


Ordinary Least Squares

Generalized Linear Models (GLM)

Logistic regression

Generalized additive models

Robust Regression

Semiparametric regression



Brain-machine interfaces

Classifying DNA sequences

Computational anatomy

Computer vision

Object recognition

Detecting credit card fraud

Internet fraud detection



Machine perception

Medical diagnosis




Optimization and metaheuristic

Recommendation and search engines

Robot locomotives

Sequence mining

Sentiment analysis

Speech and handwriting recognition

Financial market analysis

Time series forecasting

Uplift modeling


Customer relationship management

Auto insurance


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 A statistical model is updated automatically Data scientists need to run the model manually multiple times

Requirement Clarification A proper set of requirements and business justifications need to be provided A proper set of business justifications and requirements needs to be clarified

Driving Technology Machine learning is data-driven Predictive modeling is used in case driven


Work with discontinuous loss functions which are hard to differentiate, optimize and incorporate in machine learning algorithms.

The 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, need to be created before that algorithm is put to some actual use.

The need for a huge amount of data, as the more historical data, accurate is the outcome.

Need all past trends and patterns.

Polling prediction failure takes into view a specific set of parameters that are not real-time, and hence the current scenarios can influence the polling.

A lack of understanding of Human Behavior hampers HR analytics


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