Data Mining and Machine Learning Techniques: A Comprehensive Overview
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KDD-- CRISP-DM = business understanding, data understanding, data prep, modeling, evaluation, deployment-- KDD- selection, preprocessing, transformation, mining, interp/eval-- classification = most frequently used, machine learning (supervised), output is nominal or ordinal categorical in nature -- assessment methods = predictive accuracy, speed, robustnest, scalability, interpretability-- confusion matrix formulae = accuracy = (TP + TN)/(TP+TN+FP+FN); true positive rate = (TP)/(TP+FN); true negative = TN/(TN+FP); precision = (TP)/(TP+FP); recall = (TP)/(TP+FN) -- overfitting = excessively complex model, can give bad predictions, underfitting- too flexible, also gives bad predictions-- k-fold cross validation- split data into k mutually exclusive... Continue reading "Data Mining and Machine Learning Techniques: A Comprehensive Overview" »