A practitioner’s guideline for machine learning model validation. We started with statistical hypothesis testing, interpreting ANOVA table for linear regression, and explaining calculation of R-square and Adjusted R-square. Next, we discussed concepts of cross-validation, load balancing, underfitting, and overfitting in the context of regression and precision, recall and F1 score for classification followed by discussing cluster validation indices. We presented three case studies on Automotive User Interface and target prediction technology for human computer interaction.
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