TD_SVM function is a linear support vector machine (SVM) that performs classification and regression analysis on datasets.
- Regression (loss: epsilon_insensitive).
- Classification (loss: hinge). Only supports binary classification. The only response values are 0 or 1.
TD_SVM is implemented using Minibatch Stochastic Gradient Descent (SGD) algorithm, which is highly scalable for large datasets. See TD_GLM for details on SGD.
Due to gradient-based learning, the function is highly sensitive to feature scaling. Before using the features in the function, you must standardize the Input features using TD_ScaleFit and TD_ScaleTransform functions. The function only accepts numeric features. Therefore, before training, you must convert the categorical features to numeric values. The function skips the rows with missing (null) values during training.
The function output is a trained SVM model, which can be used as input to TD_SVMPredict for prediction. The model also contains model statistics of mean squared error (MSE), Loglikelihood, Akaike information criterion (AIC), and Bayesian information criterion (BIC).
Further model evaluation can be done as a post-processing step using functions such as TD_RegressionEvaluator, TD_ClassificationEvaluator, and TD_ROC.