Applies a regression XGBoost model to predict data.

This node induces a classification decision tree in main memory. The target attribute must be nominal. The other attributes used …

This node draws ROC curves for two-class classification problems. The input table must contain a column with the real class value…

Enables the aggregation of arbitrary distance measures using a Java snippet. The port number is used to refer to a value of a giv…

Distance measures on bit vectors (fingerprints).

Comprises Byte Vector related distance measures.

Enables the definition of arbitrary distance measures using a Java snippet. A distance measure is defined on two equal structured…

The Mahalanobis Distance is a metric, which measures the distance of two data sets with respect to the variance and covariance of…

The Matrix Distance is just a wrapper around a distance matrix column. This reduces the evaluation of a distance measure between …

Distance definition on numerical column(s), like for instance Euclidean or Manhattan distance. Parameters for missing value handl…

Distance definition on a string column, like for instance Levenshtein distance. Additional parameters can be set based on the sel…

This node performs supervised learning on a Keras deep learning network.

This node reads a Keras deep learning network from an input file. The file can either contain a full, pre-trained network (.h5 fi…

Writes a Keras network to a file.

Allows to manipulate the network architecture of a Keras deep learning model by choosing a new set of output tensors of the model…

Exponential linear units were introduced to alleviate the disadvantages of ReLU and LeakyReLU units, namely to push the mean acti…

A leaky ReLU is a rectified linear unit (ReLU) with a slope in the negative part of its input space. The motivation for leaky ReL…

Like the leaky ReLU, the parametric ReLU introduces a slope in the negative part of the input space to improve learning dynamics …

Like the leaky ReLU, the parametric ReLU introduces a slope in the negative part of the input space to improve learning dynamics …

The softmax function is commonly used as the last layer in a classification network. It transforms an unconstrained n-dimensional…