...predict values

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

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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

...predict values

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