Man pages for EdwinGraham/EdNet
Train deep neural networks for classification and regression
| devianceBernoulli | Function to calculate deviance for model predictions assuming... |
| devianceCategorical | Function to calculate deviance for model predictions assuming... |
| devianceGamma | Function to calculate deviance for model predictions assuming... |
| devianceNormal | Function to calculate deviance for model predictions assuming... |
| deviancePoisson | Function to calculate deviance for model predictions assuming... |
| devianceTweedie | Function to calculate deviance for model predictions assuming... |
| dfBinary | Random data for binary classification example. |
| dfGamma | Random data for Gamma distribution regression example. |
| dfPoisson | Random data for Poisson process regression example. |
| dfTweedie | Random data for compound Poisson-Gamma process regression... |
| EdNetTrain | Train a neural network model |
| normalise | Normalise a vector |
| onehotEncode | One-hot encode a factor |
| predictedClass | Converts class probabilities into a predicted class |
| predict.EdNetModel | Predict for EdNetModel objects |
| relu | Compute relu function on vector |
| sigmoid | Compute sigmoid function on vector |
