Page 28 - Internsip@TechCiti

 Principles of regression

 Splitting the data into training, validation and testing datasets

 Understanding Overfitting (Variance) vs Underfitting (Bias)

 Generalization error and Regularization techniques

 Introduction to Simple Linear Regression

 Heteroscedasticity / Equal Variance

 LINE assumption

 Collinearity (Variance Inflation Factor)

 Linearity

 Normality

 Multiple Linear Regression

 Model Quality metrics

 Deletion diagnostics

 Logistic Regression

 Multiple Logistic Regression

 Confusion matrix

 False Positive, False Negative

 True Positive, True Negative

 Sensitivity, Recall, Specificity, F1

 Receiver operating characteristics curve (ROC curve)