Predictive Analytics

Machine learning and predictive analytics are terms we hear, but what do they mean and how are they related?

Both machine learning and predictive analytics leverage data to make future predictions, but in different ways:

What is machine learning? It is a methodology where algorithms perform a specific task without explicit instructions or predetermined rules, relying on patterns and inference instead to make predictions and recalibrate as needed.

Machine learning is further broken down into supervised and unsupervised. In supervised learning, the model building process is guided by a dedicated response variable. In contrast, unsupervised learning utilizes all variables equally as it has no dedicated target.

What is predictive analytics? It is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques.

Machine Learning & Predictive Analytics Solutions at Minitab

Harness your data and gain valuable insights with Minitab's predictive analytics and machine learning capabilities.

Our predictive analytics models and tools across our suite of products can provide the accuracy, intuitive visualizations and ability to tackle complex problems.

Supervised Algorithms

Classification:

- Linear Discriminant Analysis (LDA)
- Quadratic Discriminant Analysis (QDA)
- Logistic Regression
- Classification Trees

Regression:

- Simple
- Polynomial
- Multiple
- Nonlinear
- Partial Least Squares
- Regression Trees
- Regression with Life Data
- Warranty Prediction

Time Series Methods

Unsupervised Algorithms

Clustering:

- Cluster Observations
- Cluster Variables
- Cluster K-means
- Factor Analysis

Data Reduction:

- Principal Component Analysis
- Factor Analysis

Minitab's Tree-Based Predictive Models

Our proprietary, best-in-class, tree-based machine learning algorithms not only have the power to provide deeper insights and visualize multiple complex interactions with decision trees, but are equipped to handle larger data sets with more variables, messy data, missing values, random outliers, and nonlinear relationships.

CART® (Classification & Regression Trees)

One of the most important and popular tools in modern data mining, CART is a tree-based algorithm that discovers ways to split data into smaller segments, then selects the best performing splits recursively until an optimal collection is found.

Learn more about CART

Note: The latest version of Minitab Statistical Software automatically includes CART.

Random Forests®

Based on a collection of CART Trees, Random Forests leverages repetition, randomization, sampling, and ensemble learning in one convenient place that brings together independent trees and determines the overall prediction of the forest.

Learn more about Random Forests

TreeNet® (Gradient Boosting)

Our most flexible, award-winning and powerful machine learning tool, TreeNet Gradient Boosting, is known for its superb and consistent predictive accuracy due to its iterative structure that corrects combined errors of the ensemble as it builds.

Learn more about TreeNet

Random Forests and TreeNet are both available in Minitab's Predictive Analytics Module