Explore the key features and labels in Azure Machine Learning for predicting product quality. Get insights into building effective models for quality assessment!
Question
You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.
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: Mass (kg) is a feature.
Statement 2: Quality Test is a label.
Statement 3L Temperature (C) is a label.
Answer
Statement 1: Mass (kg) is a feature: Yes
Statement 2: Quality Test is a label: Yes
Statement 3: Temperature (C) is a label: No
Explanation
Statement 1: Yes
Explanation: Mass (kg) can be a feature in a machine learning model used to predict product quality, as it’s a characteristic or attribute of the product that could influence its quality.
Statement 2: Yes
Explanation: Quality Test can be a label in this context, representing the target variable or the outcome the model seeks to predict – the quality classification or assessment of the product.
Statement 3: No
Explanation: Temperature (C) is more likely to be a feature in the dataset, providing information that might be relevant to predicting product quality, rather than a label indicating the quality itself.
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