Evaluation, Results, Analysis, Reporting
Evaluation: What and How
•Evaluate: the accuracy and generality of the model
• (we did in model evaluation, threat to validity)
•Now Evaluate: if model meets the business objectives
•Seek if there is some business reasons
•why this model is deficient
•Evaluation: Take this model and application on real world case
•See the outcome
•Evaluate: data mining/model/experiment results generated
•
Evaluation Results and Reporting
•Assess data mining results with respect to business success criteria
•Also, overall report on the result
•And then analyze/evaluate against business success criteria
•Impact/Implications on the business
•Summarize assessment results
•in terms of business success criteria
•include a final statement whether the project meets
•The initial business objectives
•Reporting and Analysis
•Examples
•Results section: Page 51
•https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1692&context=etd_projects
•Check Results and Discussion sections
•https://arxiv.org/ftp/arxiv/papers/2203/2203.06848.pdf
•A Comparative Study on Forecasting of Retail Sales
•May be complicated
•https://arxiv.org/pdf/2303.11633.pdf
•Learning Context-Aware Classifier for Semantic Segmentation
•Check results section; also Discussion Section
•https://arxiv.org/pdf/2303.07533.pdf
•You can notice: results reported under different criteria, use of tables and figures.
•Notice/read the descriptions
Data Analytics, Machine Learning, Data Science
