It's a common misconception that predictive analytics and machine learning are the same. While machine learning and predictive analytics can both leverage data to make future predictions, they do so 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 divided into two types of tasks: supervised and unsupervised. In supervised learning, the machine learning model building process is guided by a dedicated response variable. In contrast, unsupervised learning uses 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. Predictive analytics uses a variety of statistical techniques (including data mining, machine learning, and predictive modeling) to understand future occurrences.
Our proprietary, best-in-class machine learning algorithms have the power to provide deeper insights in your data.
Based on a collection of CART Trees, this algorithm uses repetition, randomization, sampling, and ensemble learning while simultaneously bringing together independent trees to determine the overall prediction of the forest.Discover Random Forests
Our most flexible, award-winning and powerful machine learning tool is known for its superb and consistent predictive accuracy due to its iterative structure that corrects combined errors of the ensemble as it builds.Discover TreeNet
Use this automated tool to easily confirm you're using the best predictive model to answer your question. Perfect for those new to predictive analytics who need recommendations and experts looking for a second opinion.Discover Automated Machine Learning
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.