Difference between revisions of "...predict values"
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I want to... | I want to... | ||
| − | |||
* ...be fast, nice line fittings, then try the [[Linear Regression]] | * ...be fast, nice line fittings, then try the [[Linear Regression]] | ||
* ...small dataset, nice line fittings, then try the [[Bayesian Linear Regression]] | * ...small dataset, nice line fittings, then try the [[Bayesian Linear Regression]] | ||
| − | * ... | + | * ...fast training, accurate, and can have a large footprint, then try the [[(Boosted) Decision Tree]] |
* ...rank ordered categories, then try the [[Ordinal Regression]] | * ...rank ordered categories, then try the [[Ordinal Regression]] | ||
* ...predict event counts, then try the [[Poisson Regression]]; log-linear | * ...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]] | * ...predict a distribution rather than point prediction with labeled data, then try the [[Fast Forest Quantile Regression]] | ||
| + | * ...accuracy matters, fit where small deviations are not penalized, then try [[Support Vector Regression (SVR)]] | ||
* ...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, 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 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)]] | ||
| + | * ...accuracy matters, I can allow long training times, then try the [[Deep Neural Network (DNN)]] | ||
| + | * ...forecast the future [[Time Series Forecasting Methods - Statistical]] or [[Time Series Forecasting - Deep Learning]] | ||
Revision as of 13:37, 7 January 2019
When a value is being predicted, as with stock prices, supervised learning is called regression.
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I want to...
- ...be fast, nice line fittings, then try the Linear Regression
- ...small dataset, nice line fittings, then try the Bayesian Linear Regression
- ...fast training, accurate, and can have a large footprint, then try the (Boosted) Decision Tree
- ...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, fit where small deviations are not penalized, then try Support Vector Regression (SVR)
- ...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)
- ...accuracy matters, I can allow long training times, then try the Deep Neural Network (DNN)
- ...forecast the future Time Series Forecasting Methods - Statistical or Time Series Forecasting - Deep Learning
