Notes
| Property | Learnings, musings, and questions starting May 2020. |
|---|
Summer Reading
Done:
- The White Tiger, by Aravind Adiga
- City of Djinns, by William Dalrymple
Going on:
- India After Gandhi, by Ram Guha
- Our Moon Has Blood Clots, by Rahul Pandita
Coming up:
- Zero: The Biography of a Dangerous Idea, by Charles Seife
- Brave New World, by Aldous Huxley
Statistical Learning: Prediction vs Inference
References:
- Intro to Statistical Learning (Springer)
It's sort of a nuanced difference. Both involve developing a 'model'. A model, can simply be viewed as the following (taking a supervised approach):
Here, X is the set of independent, predictor variables. Y is the outcome codomain; this can be a number if we're doing regression, or probability of a class in classification, and so on.
Inference, in the statistical sense used here, refers to using the model to uncover the structure of the data causal relationships between a set/subset of independent variables and the dependent variable. Eg., we can infer how a student's performance in a math test (the outcome) is dependent on...the amount of chocolate milk he drinks per day (predictor). Prediction here means predicting the outcome, or the Y when an input of the X variables is entered in the outcome. Eg. Given that a student drinks 42 glasses of chocolate milk per day, what will be his performance in the math test?