Explore automated machine learning’s efficiency in model development. Understand its iterative process, metrics, and dataset handling for optimal outcomes.
Table of Contents
Question
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Statement 1: Automated machine learning is the process of automating the time-consuming, iterative tasks of machine learning model development.
Statement 2: Automated machine learning can automatically infer the training data from the use case provided.
Statement 3: Automated machine learning works by running multiple training iterations that are scored and ranked by the metrics you specify.
Statement 4: Automated machine learning enables you to specify a dataset and will automatically understand which label to predict.
Answer
Statement 1: Automated machine learning is the process of automating the time-consuming, iterative tasks of machine learning model development: Yes
Statement 2: Automated machine learning can automatically infer the training data from the use case provided: No
Statement 3: Automated machine learning works by running multiple training iterations that are scored and ranked by the metrics you specify: Yes
Statement 4: Automated machine learning enables you to specify a dataset and will automatically understand which label to predict: No
Explanation
Box 1: Yes -Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
Box 2: No –
Box 3: Yes -During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates throughML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to “fit” your data. It will stop once it hits the exit criteria defined in the experiment.
Box 4: No -Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify. The label is the column you want to predict.
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