Difference between revisions of "...predict values"

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* ...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, 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]]  
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* ...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
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* ...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 [[Deep Neural Network (DNN)]]
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* ...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 - Data Analysis#Forecasting|Excel]]
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* ...forecast the future [[Forecasting]] using [[Excel#Excel - Forecasting|Excel - Forecasting]]
  
 
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* [[Capabilities]]
 
  
* [http://medium.com/@srnghn/machine-learning-trying-to-predict-a-numerical-value-8aafb9ad4d36 Machine Learning: Trying to predict a numerical value | Stacey Ronaghan - Medium]
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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]]
 
* [[Evaluating Machine Learning Models]]
** [http://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]
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** [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]
** [http://docs.aws.amazon.com/machine-learning/latest/dg/regression-model-insights.html Regression Model Insights |] [[Amazon | Amazon Web Services]]
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** [https://docs.aws.amazon.com/machine-learning/latest/dg/regression-model-insights.html Regression Model Insights |] [[Amazon | Amazon Web Services]]
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* [[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]
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. [http://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html Machine learning algorithms explained | Martin Heller - InfoWorld]
 

Latest revision as of 22:52, 1 March 2024

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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. Machine learning algorithms explained | Martin Heller - InfoWorld