What is the cause of the score?
Target leakage occurred in the imported dataset.
The data scientist did not fine-tune the training and validation split.
The SageMaker Data Wrangler algorithm that the data scientist used did not find an optimal model fit for each feature to calculate the prediction power.
The data scientist did not process the features enough to accurately calculate prediction power.
Explanations:
A prediction power score of 1 indicates that the feature perfectly predicts the target variable, which is often a sign of target leakage. Target leakage occurs when information from the target variable is inadvertently included in the feature set, leading to misleadingly high performance metrics.
Not fine-tuning the training and validation split could affect the model’s performance, but it would not cause a feature to have a prediction power score of 1. The score of 1 suggests a perfect prediction relationship, which is independent of how the dataset was split.
The algorithm used in SageMaker Data Wrangler is designed to evaluate the relationship between features and the target variable. If a feature has a prediction power score of 1, it implies a perfect model fit for that feature with respect to the target, indicating that the algorithm did indeed find a strong relationship rather than failing to do so.
Insufficient processing of features may lead to inaccurate calculations of prediction power, but a score of 1 indicates that the feature is already perfectly aligned with the target variable. Thus, it would not stem from a lack of processing, as the high score suggests a strong relationship.
If I had to guess, I’d say the answer is:
Target leakage occurred in the imported dataset.
Based on what I know, the answer is:
Target leakage occurred in the imported dataset.