The text discusses the concept of a target variable in supervised machine learning models. It explains that the target variable is what the model is trying to predict and can be referred to by various names. The text also highlights the importance of accurately defining the target variable and provides examples of how it can be derived from data. It advises taking sufficient time to think critically about the target variable and obtaining consensus on its definition.
What Is a Target Variable?
A target variable, also known as a dependent variable or model output, is the variable or metric that you’re trying to predict with a supervised machine learning model. It is the most important variable in any modeling project.
The Importance of Defining a Target Variable
Your target variable determines the type of modeling project you’re working on. Numeric target variables are used in regression models, while categorical variables are used in classification models. But more importantly, your target variable is the reason why you’re building a model in the first place.
Defining a Target Variable for Classification
Defining a target variable for classification can be more complex than it seems. In real-world datasets, it is rare to find a column that perfectly aligns with your target variable. You may need to make transformations to your data to engineer a target variable that accurately represents the desired classification.
Let’s take a look at some examples:
Example 1: Cancellation Date
If you’re trying to predict customer churn for a subscription product, you may not have a clear indicator of churn. In this example, you can assume that a customer has churned if their subscription end date is not null. You can assign a 1 to indicate churn and a 0 to indicate active subscription.
Example 2: Renewal Date
In this example, you have a column in your subscription table that captures when a customer becomes a paying subscriber again. Determining churn becomes more complex when customers re-subscribe within a short period after their subscription ends. You may need to define a specific lapse period, such as 30 days, to consider a customer as churned.
Why Defining the Target Variable Matters
The way you define your target variable can impact the success of your model. The accuracy of your predictions and the effectiveness of your features can depend on how well you define the target variable. It’s important to take the time to think critically about your target variable and align it with your business goals.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- The Hardest Part: Defining A Target For Classification
- Towards Data Science – Medium
- Twitter – @itinaicom
