Understand machine learning tasks: from feature engineering to model evaluation and deployment. Learn how feature selection impacts weather model training.
Match the machine learning tasks to the appropriate scenarios. To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point. Select and Place:
- Feature engineering
- Feature selection
- Model deployment
- Model evaluation
- Model training
- Examining the values of a confusion matrix
- Splitting a date into month, day, and year fields.
- Pricing temperature and pressure to train a weather model.
- Model evaluation: Examining the values of a confusion matrix
- Feature engineering: Splitting a date into month, day, and year fields.
- Feature selection: Pricing temperature and pressure to train a weather model.
Box 1: Model evaluation -The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.
Box 2: Feature engineering -Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization. Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
Box 3: Feature selection -In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
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