What is Logistic Regression? #9

Week-9 R and data files: https://github.com/bkrai/Statistical-Modeling-and-Graphs-with-R TIMESTAMPS 00:00 Introduction & Logistic regression examples 07:13 Linear regression versus logistic regression 12:00 Logit 16:06 Log odds 19:38 Probability equation 22:11 Interpreting odds, probability 24:05 Example - student applications 25:22 Logistic regression model 26:52 Working with R 33:05 Split data 34:00 Logistic regression in R 38:00 Predicting probabilities and using probability equation for calculation 47:40 Termplot 54:37 Confusion matrix and misclassification error for training data 59:40 Confusion matrix and misclassification error for testing data 01:02:17 Predicting model essentials 01:02:46 Regression Vs classification 01:04:46 Data partitioning 01:06:18 Predictive model sequence 01:06:45 Model performance assessment & model selection 01:10:51 Model fit versus complexity 01:12:03 Some assessment strategies 01:12:33 Decision matrix or confusion matrix 01:13:36 Decision matrix or confusion matrix - training data 01:13:53 Decision matrix or confusion matrix -testing data 01:14:12 Is 80% accuracy good? 01:15:20 Two models with same accuracy 01:17:21 What is baseline rate? Calculation in R 01:19:41 Sensitivity 01:20:02 Specificity R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular. #LogisticRegression #LogOdds #MachineLearning