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Answer: Feature Importance Ranking
The correct answer is **Feature Importance Ranking**. This technique is pivotal in machine learning projects aimed at feature selection for boosting model efficiency. It involves algorithms that assign importance scores to features based on their contribution to the model's predictive accuracy. For instance, tree-based models such as Random Forests utilize this method to rank features by their impact on the model's performance. By focusing on the most influential features, this approach can significantly enhance the efficiency and effectiveness of your machine learning models.
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In your machine learning project, you're focusing on feature selection to enhance model efficiency. Which of the following techniques, supported by Databricks MLlib, is designed to pinpoint the most significant features in your dataset?
A
Principal Component Analysis
B
Feature Scaling
C
Feature Importance Ranking
D
Recursive Feature Elimination