Difference between revisions of "...predict values"
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| − | + | [[AI Solver]] | |
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I want to... | I want to... | ||
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* ...be fast, nice line fittings, then try the [[Linear Regression]] | * ...be fast, nice line fittings, then try the [[Linear Regression]] | ||
| + | * ...analyze multiple [[Regression]] data with large variances [[Ridge 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]] | + | * ...predict event counts, then try the [[Poisson Regression]]; log-linear |
| − | * ...predict a distribution 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, I can NOT accept a long training time, and have little memory, then try a [[Decision Forest 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, then try a [[ | + | * ...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 [[Gradient Boosting Machine (GBM)]] 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 [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]] | ||
| + | * ...forecast the future [[Forecasting]] using [[Excel#Excel - Forecasting|Excel - Forecasting]] | ||
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| + | ___________________________________ | ||
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| + | * [https://medium.com/@srnghn/machine-learning-trying-to-predict-a-numerical-value-8aafb9ad4d36 Machine Learning: Trying to predict a numerical value | Stacey Ronaghan - Medium] | ||
| + | * [[Evaluating Machine Learning Models]] | ||
| + | ** [https://bookdown.org/max/FES/engineering-numeric-predictors.html Feature Engineering and Selection: A Practical Approach for Predictive Models - 6 Engineering Numeric Predictors | Max Kuhn and Kjell Johnson] | ||
| + | ** [https://docs.aws.amazon.com/machine-learning/latest/dg/regression-model-insights.html Regression Model Insights |] [[Amazon | Amazon Web Services]] | ||
| + | * [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]] | ||
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| + | When a value is being predicted, as with stock prices, supervised learning is called regression. Prediction problems (e.g. What will the opening price be for shares tomorrow?) are a subset of regression problems for time series data. [https://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html Machine learning algorithms explained | Martin Heller - InfoWorld] | ||
Latest revision as of 23:52, 1 March 2024
I want to...
- ...be fast, nice line fittings, then try the Linear Regression
- ...analyze multiple Regression data with large variances Ridge 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 Gradient Boosting Machine (GBM) 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 Forecasting using Excel - Forecasting
___________________________________
- Machine Learning: Trying to predict a numerical value | Stacey Ronaghan - Medium
- Evaluating Machine Learning Models
- Excel ... Documents ... Database; Vector & Relational ... Graph ... LlamaIndex
When a value is being predicted, as with stock prices, supervised learning is called regression. Prediction problems (e.g. What will the opening price be for shares tomorrow?) are a subset of regression problems for time series data. Machine learning algorithms explained | Martin Heller - InfoWorld
