In Artificial Intelligence and, more specifically, in Machine Learning, a model represents a decision process in an abstract manner. The model’s primary goal is to enable automation of the decision process, often applied to business. And in some cases, the model helps with understanding the modeled process itself. Machine Learning models are mathematical algorithms that are “trained” using data. Ideally, the model should also explain the reason behind its decision to help understand the decision process (though often challenging).
Data models have a specific purpose. We can classify them as follows:
If archeologists can use predictive models to discover never unearthed spots, imagine what high-level predictive models can do for healthcare issues, trading algorithms, or customer relationship management. Predictive models can provide meaningful analytics, and thanks to their anticipation ability, help gain competitive edges.
Descriptive models are an abstract representation of the system they model. They enable a better understanding of the relationships within this system, which might be customer-driven, and the interactions between internal and external events or behaviors. It’s useful to optimize a workflow to improve active customers’ ROI, for example.
Fed by qualitative and quantitative data, we build decision models to help us make decisions. They help us perceive, organize, and manage the business rules. Planning, prices, logistics can benefit from decision models.
Regarding predictive modeling, we can distinguish different tasks depending on the nature of the variable to predict. If the variable is continuous, it is a regression task, and in that case, the models return an actual value. If the variable is discrete and divided into categories, it is a classification task, and the models deliver a class. When there are two classes, we talk about binary classification and multi-classification otherwise.
Several algorithms exist to build models able to perform regression and classification tasks: regression algorithms, Bayesian algorithms, kernel algorithms, decision trees, neural networks, and evolutionary algorithms such as ZGP (the core engine of TADA).
Numerous algorithms and engines exist nowadays, expanding the scope of available possibilities. Amongst them:
Regression algorithms gather supervised machine learning techniques, where algorithms are trained before being applied to data to create a prediction. They are useful to assess the causal effect of a (or multiple) variable upon another.
Part of supervised machine learning technique as well, decision trees are used to predict a goal or a target based upon a series of questions. It can operate through classification (categories) or regression (numbers).
Time series are used to comprehend the behavior of a given asset over time, and therefore build accurate predictions about its future. It is done by indexing series of data points in time order, whether they are listed or graphically represented.
Small Data is a new frontier in data. It represents up to 85% of all the data collected. It then challenges the capability to create algorithms capable of working on datasets with little or no history and yet being able to provide meaningful insights through efficient predictive modeling.
ZGP is a unique mathematical expression engine inspired by evolutionary algorithms. It is able to create simple mathematical expressions that are particularly good at predicting or classifying based on small datasets.
Algorithms and engines are an ever evolving topic in the AI world. Stay tuned for more insights.
Data analysis is the process that converts raw data into usable insights.
Sensitivity analysis allows one to assess the causal effect of a (or multiple) variable(s) upon one another. It helps test the robustness of a model and optimize it. It does so by assessing the uncertainty caused by a given variable.
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