6 Steps to Perfect Your Machine Learning Model’s Target Variable

In the world of data science, the accuracy of a machine learning model hinges on the correct definition of its target variable. The SCORES framework offers a systematic approach to ensure that your model’s target aligns with business objectives. Here’s how it works:

• Business Alignment: Start by understanding the business goals. For instance, FinTech First, a digital lending startup, aimed to automate credit approvals by identifying risky applicants.
• Metric Selection: Choose metrics that reflect business outcomes. FinTech First combined outstanding balance with credit limit percentage to balance risk assessment.
• Measurement Type: Decide between absolute or relative metrics. A relative metric was more suitable for FinTech First to account for diverse customer profiles.
• Event Window: Define the time frame for predictions. Shorter windows yield more confident predictions but limit business decision-making time. FinTech First found that a 30-day window was optimal for predicting default risk.
• Threshold Setting: Establish thresholds to differentiate between significant and insignificant events. For FinTech First, a past-due amount over $100 or 1% of the credit limit signaled a risky customer.
• Validation: Validate the target variable’s impact on business outcomes. This step ensures the model’s predictions are actionable and aligned with business strategy.

The SCORES framework not only streamlines the process of defining target variables but also ensures that the model’s predictions are both accurate and relevant to business needs, reducing rework and enhancing model adoption.

Source: towardsdatascience.com

Related Videos

Use This Way Of Training Machine Learning Models For Efficiency

95 Making Predictions With Our Model | Scikit-learn Creating Machine Learning Models

Machine Learning Algorithm- Which one to choose for your Problem?

Target Variable | Python for ML in Finance | Free Quantra Course

Why do we split data into train test and validation sets?

Classification and Regression in Machine Learning