Video Introduction [1:45]
Supervised learning consists of forecasting the values of numerical variables as well as classifying examples into groups. This week we begin a two-week segment on classification into one of two groups. The estimation procedure for this week, how the machine learns, is logistic regression, invented in the 1930's.
We also experience another benefit of the sklearn Python environment. We saw last week the relative simplicity of doing a multiple regression analysis with sklearn. When we move to a new model estimator, logistic regression, many of the basic implementation steps are identical to what we did with standard multiple regression.
video [3:49] 10.1, Overview
video [9:17] 10.2: Binary Classification
video [10:42] 10.3: Classification Fit Indices
video [29:31] 10.4: Logistic Regression
pdf [template]
There is one video, divided into chapters. Use the three line menu at the top-left of the screen to move directly to a chapter.
video [34:51]