What Machine Learning model templates are available?

The AI & Analytics Engine offers a variety of high-quality machine learning model templates.

The model templates are grouped according to the ML approach;

1. Supervised and;
2. Time-series forecasting.
   

Supervised Learning 

Supervised machine learning is an approach using labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately.  Using paired inputs and labels, these models can be properly evaluated and improved with more data available over time.

Regression Model Templates/Algorithms

1. AdaBoost regressor

2. Bayesian ridge regression

3. Decision tree regressor

4. Extra-trees regressor

5. Gradient boosting regressor

6. K-Nearest neighbors regressor

7. LightGBM regressor

8. Linear regression (with support of GPU, multi-GPU)

9. Ridge regression (with support of multi-GPU)

10. Mini-batch SGD (stochastic gradient descent) regressor (with support of GPU)

11. Random forest regressor (with support of GPU, multi-GPU)

12. XGBoost Regressor (with support of GPU, multi-node, multi-GPU)

Classification Model Templates/Algorithms

1. AdaBoost classifier

2. Random forest classifier (with support of GPU, multi-GPU)

3. K-Nearest neighbors classifier

4. Logistic regression (with support of GPU)

5. Mini-batch SGD (stochastic gradient descent) classifier (with support of GPU)

6. Decision tree classifier

7. Extra-trees classifier

8. Gradient boosting classifier

9. LightGBM classifier

10. Gaussian Naive Bayes

11. XGBoost classifier (with support of GPU, multi-node, multi-GPU)

Time Series Forecasting

Time series forecasting is the use of algorithms (model templates) to predict future values based on historical time-stamped data. The Engine supports the following algorithms (model templates): 

1. ARIMA (Autoregressive Integrated Moving Average)

2. Prophet

3. ETS (Error, Trend, Seasonality)

4. TBATS (Trigonometric regressors, Box-Cox transformations, ARMA errors, Trend, Seasonality)

5. Theta models