Authentic Databricks Certification Exam Databricks-Machine-Learning-Associate Questions Answers

The method that transforms categorical features into a series of binary indicator variables is known as one-hot encoding. This technique converts each categorical value into a new binary column, which is essential for models that require numerical input. One-hot encoding is widely used because it helps to handle categorical data without introducing a false ordinal relationship among categories.References:

• Feature Engineering Techniques (One-Hot Encoding).

If the model object is passed to the estimator parameter ofCrossValidatorand the cross-validation object itself is placed as a stage in the pipeline, the feature engineering stages within the pipeline would be applied separately to each training and validation fold during cross-validation. This leads to a significant issue: the feature engineering stages would be computed using validation data, thereby leaking information from the validation set into the training process. This would potentially invalidate the cross-validation results by giving an overly optimistic performance estimate.References:

• Cross-validation and Pipeline Integration in MLlib (Avoiding Data Leakage in Pipelines).

The suggestion not to one-hot encode categorical feature variables within the feature repository is justified because one-hot encoding can be problematic for some machine learning algorithms. Specifically, one-hot encoding increases the dimensionality of the data, which can be computationally expensive and may lead to issues such as multicollinearity and overfitting. Additionally, some algorithms, such as tree-based methods, can handle categorical variables directly without requiring one-hot encoding.

References:

• Databricks documentation on feature engineering: Feature Engineering