# ...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 Time Series Forecasting Methods - Statistical or Time Series Forecasting - Deep Learning

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- Capabilities
- Excel FORECAST
- Machine Learning: Trying to predict a numerical value | Stacey Ronaghan - Medium

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