Difference between revisions of "...predict values"
Line 1: | Line 1: | ||
* [[AI Solver]] | * [[AI Solver]] | ||
− | * [[Capabilities]] | + | * [[Capabilities]] |
+ | * [[Excel FORECAST] | ||
When a value is being predicted, as with stock prices, supervised learning is called regression. | When a value is being predicted, as with stock prices, supervised learning is called regression. | ||
Line 17: | Line 18: | ||
* ...accuracy matters, I can NOT accept a long training time, then try a [[Boosted Decision Tree Regression]] with large memory fooprint | * ...accuracy matters, I can NOT accept a long training time, then try a [[Boosted Decision Tree Regression]] with large memory fooprint | ||
* ...accuracy matters, I can accept a long training time, then try a [[General Regression Neural Network (GRNN)]] | * ...accuracy matters, I can accept a long training time, then try a [[General Regression Neural Network (GRNN)]] | ||
− | |||
− | |||
− | |||
− | |||
− |
Revision as of 09:47, 29 July 2018
- AI Solver
- Capabilities
- [[Excel FORECAST]
When a value is being predicted, as with stock prices, supervised learning is called regression.
___________________________________
I want to...
- ...be fast, nice line fittings, then try the Linear Regression
- ...small dataset, nice line fittings, then try the Bayesian Linear Regression
- ...fit where small deviations are not penalized, then try Support Vector Regression (SVR)
- ...rank ordered categories, then try the Ordinal Regression
- ...predict event counts, then try the Poisson Regression; log-linear
- ...predict a distribution rather than point prediction with labeled data, then try the Fast Forest Quantile Regression
- ...accuracy matters, I can NOT accept a long training time, and have little memory, then try a Decision Forest Regression
- ...accuracy matters, I can NOT accept a long training time, then try a Boosted Decision Tree Regression with large memory fooprint
- ...accuracy matters, I can accept a long training time, then try a General Regression Neural Network (GRNN)