Databricks Machine Learning Associate Question 586
Select 3You are training a regression model on a dataset using Databricks. To evaluate your model's performance and ensure it generalizes well, you decide to use k-fold cross-validation. After configuring a pipeline for cross-validation, which of the following steps will be performed during this process?
- A
The dataset is split into k equally sized folds, and each fold is used as a validation set exactly once.
- B
All k models generated during cross-validation are automatically averaged to produce the final model.
- C
For each fold, the model is trained on k-1 folds and validated on the remaining fold.
- D
The hyperparameters of the model are optimized during the cross-validation process.
- E
The cross-validation process outputs an overall validation metric by averaging the metrics from each fold.
Show answer and explanation
Correct answers: A, C, E
Explanation
Cross-validation is a method used to evaluate a model's performance by splitting the dataset into k folds. Each fold is used as a validation set while the remaining folds are used for training. The process ensures all data points are used for both training and validation. It outputs an averaged metric across folds for performance evaluation, but it does not automatically optimize hyperparameters or produce a final combined model.
- A. Correct.
Correct: Cross-validation involves splitting the dataset into k folds and using each fold as a validation set once, ensuring that all data points are used for both training and validation.
- B. Incorrect.
Incorrect: Cross-validation does not automatically combine all k models into a final model. It is primarily used for evaluation, not model aggregation.
- C. Correct.
Correct: The training process during cross-validation involves using k-1 folds for training and the remaining fold for validation, repeated k times.
- D. Incorrect.
Incorrect: While hyperparameter optimization can be performed using cross-validation, it is not an inherent part of the cross-validation process itself. Extra configuration is required for hyperparameter tuning.
- E. Correct.
Correct: Cross-validation computes metrics for each fold and then averages them to provide an overall evaluation metric.