AutoGrab Confidence Score

Explanation of the Confidence Score returned in AutoGrab valuations and how it is determined

AutoGrab’s confidence score is derived from a statistical process that reflects how confident the valuation model is in its predicted value. The score ranges between 0 and 1, where higher values indicate stronger confidence in the accuracy of the prediction, and lower values suggest greater uncertainty.

Interpreting the Confidence Score

• High Confidence (e.g., >0.8): The valuation model is highly certain about the estimated retail value, supported by a large dataset and strong model accuracy.

• Medium Confidence (e.g., 0.5–0.8): The prediction is reasonable but may require additional validation due to a smaller sample size or price volatility.

• Low Confidence (e.g., <0.5): The prediction is uncertain, often due to limited data availability or high market volatility.

Factors Affecting Confidence Scores

• Data Quality and Availability:

  • High-quality, complete data for a vehicle variant increases the confidence score.

  • Listings with poor data quality (e.g., missing mileage, unknown price) are excluded from AutoGrab’s valuation model.

• Model Fit:

  • Vehicles closely aligned with the training data generally have higher confidence scores.

  • Outliers and rare vehicle types may result in lower confidence.

• Recency of Data:

  • AutoGrab incorporates recency weighting in its valuation model.

  • Vehicles with more recent and high number of listings achieve higher confidence scores.

Applications of Confidence Scores

• Informed Decision-Making: AutoGrab’s confidence score helps customers determine whether additional inspection or pricing validation is needed before acquiring a vehicle.

• Risk Mitigation: A low confidence score highlights higher risks, enabling AutoGrab’s customers to approach vehicle transactions with caution and avoid potential losses.

• Enhanced Transparency: Including confidence scores alongside valuations enhances trust and confidence with AutoGrab’s clients, allowing them to assess the reliability of predictions.

This framework ensures AutoGrab provides reliable valuations while highlighting areas that may require further analysis.