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