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Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.
Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.
Covers the Hedonic Pricing Method for assessing implicit prices of goods and introduces the Contingent Valuation Method for estimating environmental goods' value.
Explores data quality in Life Cycle Assessment, covering inventory format, control, measurement procedures, uncertainty factors, and the Data Quality Rating system.
