Predictive Analytics - Machine Learning for Everyone
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.
Boost your analytical power with best-in-class, accurate machine learning algorithms that provide deeper insights into your data.
Automated Machine Learning
Easily confirm you’re using the best predictive model to answer your question with Automated Machine Learning. Perfect for those new to predictive analytics who need recommendations and experts looking for a second opinion.
CART® (Classification & Regression Trees)
One of the most popular tools in modern data mining, this tree-based algorithm discovers how to split data into smaller segments, then selects the best performing splits repeatedly until an optimal collection is found.
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.
TreeNet® (Gradient Boosting)
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.
“I usually stick to the methods that have always worked for me—but TreeNet partial dependency plots have given me greater insight and helped solve some of my most vexing problems. ”
– Process Engineer, Consumer Packaged Goods
“Engineers and analysts can spend 80% of their time trying to identify the important drivers of process issues when performing a root cause analysis.”
– Market Research from Minitab
“Our Continuous Improvement team has made great progress with Minitab’s predictive analytics. The integration of data science and CI has led to more predictable KPI’s.”
– Data Science Leader, Food Manufacturer
And so much more...
SWOT Analysis, Gantt Chart, Meeting Minutes, CTQ Trees, Five Whys, Idea Map, SIPOC, Audit Plan, Control Plan…
Ready for a demo of Minitab Statistical Software?