Databricks Certified Machine Learning - Associate

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In the context of AutoML, explain the role of data preprocessing and how AutoML can automate this process. Provide a detailed description of the techniques used by AutoML for data preprocessing and their significance in improving model performance.

Simulated

Data preprocessing is not necessary in AutoML, as the algorithm can handle raw data without any preprocessing.

7.7%

Data preprocessing is important in AutoML, but it is performed manually by the user by cleaning the data, handling missing values, and encoding categorical variables.

7.7%

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Data preprocessing is important in AutoML, and it can be automated using techniques such as data cleaning, handling missing values, encoding categorical variables, and feature scaling to improve the quality and format of the data.

80.8%

Data preprocessing is not relevant for AutoML, as the algorithm automatically selects the best model and hyperparameters without considering the quality of the data.

3.8%