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Answer: Data preprocessing, feature selection, model selection, hyperparameter tuning, and model evaluation.
AutoML automates several critical steps in the machine learning workflow, including data preprocessing to ensure data quality, feature selection to identify the most relevant features, model selection to choose the best algorithm, hyperparameter tuning to optimize model performance, and model evaluation to assess the model's effectiveness. These steps collectively enhance the efficiency and effectiveness of the model development process by reducing manual effort and potential human error.
Author: LeetQuiz Editorial Team
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In the context of AutoML, describe the detailed steps involved in the machine learning workflow that are typically automated. Discuss how each step contributes to the overall efficiency and effectiveness of the model development process.
A
Data preprocessing, feature selection, model selection, hyperparameter tuning, and model evaluation.
B
Data collection, data cleaning, feature engineering, model training, and model deployment.
C
Data ingestion, data transformation, model training, model validation, and model optimization.
D
Data sampling, feature extraction, model initialization, model testing, and model refinement.
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