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- Go to itemThis node induces a classification decision tree in main memory. The target attribute must be nominal. The other attributes used …0
- Go to itemThis node draws ROC curves for two-class classification problems. The input table must contain a column with the real class value…0
- Go to itemEnables the aggregation of arbitrary distance measures using a Java snippet. The port number is used to refer to a value of a giv…0
- Go to itemEnables the definition of arbitrary distance measures using a Java snippet. A distance measure is defined on two equal structured…0
- Go to itemThe Mahalanobis Distance is a metric, which measures the distance of two data sets with respect to the variance and covariance of…0
- Go to itemThe Matrix Distance is just a wrapper around a distance matrix column. This reduces the evaluation of a distance measure between …0
- Go to itemDistance definition on numerical column(s), like for instance Euclidean or Manhattan distance. Parameters for missing value handl…0
- Go to itemDistance definition on a string column, like for instance Levenshtein distance. Additional parameters can be set based on the sel…0
- Go to itemThis node reads a Keras deep learning network from an input file. The file can either contain a full, pre-trained network (.h5 fi…0
- Go to itemAllows to manipulate the network architecture of a Keras deep learning model by choosing a new set of output tensors of the model…0
- Go to itemExponential linear units were introduced to alleviate the disadvantages of ReLU and LeakyReLU units, namely to push the mean acti…0
- Go to itemA leaky ReLU is a rectified linear unit (ReLU) with a slope in the negative part of its input space. The motivation for leaky ReL…0
- Go to itemLike the leaky ReLU, the parametric ReLU introduces a slope in the negative part of the input space to improve learning dynamics …0
- Go to itemLike the leaky ReLU, the parametric ReLU introduces a slope in the negative part of the input space to improve learning dynamics …0
- Go to itemThe softmax function is commonly used as the last layer in a classification network. It transforms an unconstrained n-dimensional…0