AI regression model is a predictive technique that analyzes the relationship between a dependent and an independent variable in data. It occurs mainly to demonstrate whether or not there is a linear relationship between the two variables.
Currently, artificial intelligence and machine learning perform this analysis technique to determine the predictor’s strength, forecast trends, time series and in the case of cause-effect relationships. We explain this below.
What exactly is an AI regression model?
They are AI algorithms that provide a descriptive function of the relationship between one or more independent variables and a response variable.
Let’s see it in an example:
A linear regression model can describe the relationship between height and weight—likewise the prediction of the effects of pollution or fuel efficiency.
This regression model can be the basis for many types of prediction so that we can determine the effects that one variable may or may not have on other variables.
Types of regression you can apply
* Linear regression:
In this regression model, the relationship between the input and output variables is in a straight line. There is a predictor variable and a dependent variable. This is the easiest model to conceptualize since we can even observe it in the real world.
Our brain is configured to find a linear pattern between two variables, even if they are not linked.
If the data includes more than one independent variable, the linear regression is called a multiple linear regression model.
If we send a direct message to 1000 people through one of our marketing channels and get 5 responses. We could model by linear regression that if we send 2000 messages, we could get at least 10 responses, and so on.
Actually, this number can vary for many other reasons, but if we analyze it under the linear regression model, this would be the result.
* Multiple regression model:
This regression model is applied when there is more than one input variable that can affect the result.
By having more than one input variable, it is possible to know what effect each input has on the target, and how they are combined to produce different results. In this regression model, the relationship between the variables can remain linear.
Taking the same example above of the 1000 direct messages, under this regression model, we can have more precise information about why, when and reasons why people respond or not to the message we send as part of the marketing strategy. This allows your team to understand the timing, frequency and what drives potential customers to respond. In that way, take action to have more frequent responses. This is an example of the AI multiple regression model’s usefulness.
* Non-linear regression model
It is an analysis model where data is fitted to a model and then expressed as a mathematical function. Non-linear regression relates the two variables in a non-linear but curved relationship.
Considering the marketing messages example mentioned above, as we increase the number of messages, the responses decrease compared to the number of messages sent. We would use a non-linear regression model to find a positive relationship between the number of emails and the response. As the number of emails increases, the model will flatten out and become almost constant.
* Stepwise Regression Modeling
Stepwise regression is a different technique, unlike the previous ones which were models. It is a process to obtain the most accurate model, in which there are many input variables, so we eliminate less significant variables to arrive at the desired model.
In the marketing messages example, we would first consider the number of messages we sent, then some additional data such as the recipient’s average age, and in the third step, the number of messages that each person received from us. Each variable we add will add precision to this stepwise model that is applied.
Why is it relevant for your company to apply AI regression models?
With an AI regression model, knowing the relationship between the causes and effects of the strategies that you apply in your company will allow you to make essential decisions based on indicators, and once you apply them, evaluate the performance they have had on the results.
For example: Imagine you launch a new service, the price and the sales number are usually correlated, although there are also many variables to consider, such as value proposition, communication and sales strategies, etc… We can model these by using regression models to improve the launch results.
Caveat: Correlation is a “hint” of causation between variables and not proof. A strong correlation may indicate causation, but it is also likely that there are other explanations:
- It may be the result of chance: the variables appear to be related, but in reality, there is no underlying relationship.
- There may be a third variable lurking that makes the relationship appear stronger (or weaker) than it is.
The correlation only shows us that the two factors are related, not that a change in one directly affects the other.