Databricks Machine Learning Associate Question 415
Select 3A data scientist is tasked with building a machine learning model to predict customer churn. They decide to use Databricks AutoML to streamline the process. How does AutoML facilitate model and feature selection in this scenario?
- A
It automatically evaluates multiple machine learning algorithms to identify the best-performing model.
- B
It provides a ranked list of features based on their importance to the model's performance.
- C
It allows the user to manually choose the best features and algorithms through an interactive interface.
- D
It performs hyperparameter tuning to optimize the selected model's performance.
- E
It eliminates the need for any human intervention during the model-building process.
Show answer and explanation
Correct answers: A, B, D
Explanation
Databricks AutoML facilitates model and feature selection by automating the evaluation of multiple algorithms, reporting feature importance, and performing hyperparameter tuning. This streamlines the model-building process while still allowing users to interpret results and make informed decisions. However, it does not fully eliminate the need for human involvement or rely on manual intervention for selection.
- A. Correct.
Correct: AutoML evaluates various machine learning algorithms and selects the one that yields the best performance based on the given dataset.
- B. Correct.
Correct: AutoML generates insights on feature importance, which helps in understanding and ranking the contribution of different features to the model.
- C. Incorrect.
Incorrect: AutoML automates the process of feature selection and model evaluation. While it provides insights, it does not rely on manual selection by the user through an interactive interface.
- D. Correct.
Correct: AutoML includes hyperparameter tuning as part of its workflow to improve the performance of the selected model automatically.
- E. Incorrect.
Incorrect: While AutoML automates many aspects of the model-building process, human intervention may still be needed for tasks like data preparation, validation, and interpreting results.