Databricks Databricks-Machine-Learning-Associate Exam With Confidence Using Practice Dumps

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).

One-hot encoding transforms categorical variables into a format that can be provided to machine learning algorithms to better predict the output. However, when done prematurely or universally within a feature repository, it can be problematic:

• Dimensionality Increase:One-hot encoding significantly increases the feature space, especially with high cardinality features, which can lead to high memory consumption and slower computation.
• Model Specificity:Some models handle categorical variables natively (like decision trees and boosting algorithms), and premature one-hot encoding can lead to inefficiency and loss of information (e.g., ordinal relationships).
• Sparse Matrix Issue:It often results in a sparse matrix where most values are zero, which can be inefficient in both storage and computation for some algorithms.
• Generalization vs. Specificity:Encoding should ideally be tailored to specific models and use cases rather than applied generally in a feature repository.

References

• "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Max Kuhn and Kjell Johnson (CRC Press, 2019).

Ensemble learning is a machine learning technique that involves combining several models to solve a particular problem. The scenario described fits the concept of ensemble learning, where two models, each performing well under different conditions, are combined to create a more robust model. This approach often leads to better performance as it combines the strengths of multiple models.

References

• Introduction to Ensemble Learning: https://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/