What term describes the value the target would take if the feature were zero, representing bias in the model?

What term describes the value the target would take if the feature were zero, representing bias in the model?

• A. Intercept
• B. Slope
• C. Data Sensitivity
• D. Functional Forms

Answer: (A)

The intercept is the baseline value the model predicts when the feature is zero. In a simple linear model y = intercept + slope × x, setting x to zero leaves y equal to the intercept. This intercept acts as the bias term, representing the prediction the model would make without the feature’s influence. The slope, on the other hand, describes how much the prediction changes as the feature changes. Terms like data sensitivity or functional forms refer to other aspects of the model, not the zero-input baseline.

The intercept is the baseline value the model predicts when the feature is zero. In a simple linear model y = intercept + slope × x, setting x to zero leaves y equal to the intercept. This intercept acts as the bias term, representing the prediction the model would make without the feature’s influence. The slope, on the other hand, describes how much the prediction changes as the feature changes. Terms like data sensitivity or functional forms refer to other aspects of the model, not the zero-input baseline.