BIS.Net Analyst | Analytics Apps For Everday Managers

Introducing Machine Learning

‘Machine Learning’ technology differs from traditional analytic technology with less reliance on formula and considerable use of algorithms making use of the number crunching power of computers (the machine). This opens many opportunities, because mathematical formulae and statistical functions cannot adequately cope with most modern real-world complexities, relying un-unrealistic assumptions.

BIS.Net Analyst brings MACHINE LEARNING to EVERYDAY MANAGERS

Machine Learning is premium technology. Premium technology, traditionally applied by IT departments in large corporations, can now be applied by non-IT managers, even in small sized companies, without managers needing to understand the underlying theory.

Organizations benefit because they no longer need to rely on IT departments. Now ideas can flow in from managers who can evaluate these ideas, leading to far more improvements compared to relying on specialist departments who are often resource constrained.

This premium technology can be applied in almost every conceivable environment, such as production, quality, marketing, sales, research and development, health and fitness, hospitals, and the home. Links are provided below to help identify applications.

The input and output provided by BIS.Net Analyst's Machine Learning App has been engineered to be as simple and effective as possible for managers who do not understand (often unnecessary) complex jargon used by specialist IT professionals.

What's included in the Machine Learning App?

Binary Classification

Used to predict an outcome that can take on only two values from few or many variables that can affect the outcome

Cluster Analysis

Used to group objects such that each group is more similar than those in other clusters. Cluster analysis is used in many areas such as market research, quality assurance, social network analysis, medical research, pandemics.

Forecasting

Univariate Forecasting used to forecast time series values with seasonal, cyclic and trend components. Can be used for sales and energy consumption and other time-based data.

Multiclass Classification

Like binary classification except that outcome can take on more than two values, but for each example only a single class label is predicted. For example, we can use multi class classification to predict who from 3 political parties will win the election based on a wide range of factors.