Regression & Model Building PDF
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This document covers regression and model building techniques. It details topics such as ordinary least squares estimation, multiple linear regression, and model validation. The document also discusses classification algorithms and evaluation metrics.
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UNIT-III: Regression & Model Building: Introduction, Ordinary Least Squares Estimation for Multiple Linear Regression, Multiple Linear Regression Model Building, Partial Correlation and Regression Model Building, Interpretation of Multiple Linear Regression Coefficients. Validation of Multiple Regre...
UNIT-III: Regression & Model Building: Introduction, Ordinary Least Squares Estimation for Multiple Linear Regression, Multiple Linear Regression Model Building, Partial Correlation and Regression Model Building, Interpretation of Multiple Linear Regression Coefficients. Validation of Multiple Regression Model, Coefficient of Multiple Determination (R-Squared), Adjusted R-Squared, Statistical Significance of Individual Variables in Multiple Linear Regression: t-Test, Validation of Overall Regression Model: F-Test. UNIT-IV: Classification Algorithms: What is Classification? General Approach to Classification, k-Nearest Neighbor Algorithm, Logistic. Regression, Decision Trees, Naive Bayesian Classifier, Ensemble Methods: Bagging, Boosting and AdaBoost and XBoost, Random Forests Support Vector Machines, Rough Set and Fuzzy Set Approaches, Classification Model Evaluation and Selection: Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value.
