Supervised vs unsupervised learning, regression, classification, overfitting, and metrics.
Principal component analysis creates orthogonal directions that capture decreasing amounts of variance in the fitted data. It can reduce dimensionality and correlated numeric features, often after sui...
Evaluate both models on the same suitable splits and metric, using identical preprocessing boundaries and a meaningful baseline. Examine paired differences, uncertainty, and practical effect size rath...
Models based on distances, regularized coefficients, or gradient-based optimization can be strongly affected by the relative scale of features. A feature measured in thousands can dominate one measure...
Keep all rows for one patient in the same evaluation partition when the objective is generalization to unseen patients. A random row split can let the model recognize patient-specific patterns from tr...
Classification predicts a category or category probability, such as whether a customer will cancel a subscription. Regression predicts a numerical quantity, such as next month’s energy usage. The busi...
Training data fits model parameters. Validation data guides model selection, feature decisions, and hyperparameters. Test data provides a final assessment after those choices are fixed. Repeatedly usi...
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