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.
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.
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.
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
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
Minitab's Predictive Analytics Module is just part of what we have to offer around predictive analytics and machine learning.
MARS® Capture nearly undiscoverable essential nonlinearities and interactions with the machine learning model most similar to traditional regression.
Better your predictive analytics and machine learning models with feature engineering, the way to process and prepare your data before you begin.
Access best practices, success stories, real-life examples, and how-to advice as you enter the world of machine learning and predictive analytics.