Feature Effects¶

Feature Effects answers the question—how does a model "understand" the relationship between each feature and the target? It is an on-demand feature, dependent on the Feature Impact calculation, which is prompted for when first opening the visualization. The insight is communicated in terms of partial dependence, an illustration of how changing a feature's value, while keeping all other features as they were, impacts a model's predictions.

Next steps¶

Pair Feature Effects with these related insights to build a fuller picture of how the model uses its features.

• Feature Impact: See the high-level ranking of which features most strongly drive model decisions, the calculation Feature Effects depends on.
• Individual Prediction Explanations: Drill into row-by-row detail on how much each feature contributes to a single prediction.
• Coefficients: Review the positive or negative impact of variables on a linear model's predictions.