#Nn models sets how to#

With the above mentioned defintions the SimpleMatchingSimilarity is: (e+z)/(e+u+z) With the above mentioned defintions the RussellRaoSimilarity is: e/(e+u+z) With the above mentioned defintions the RogersTanimotoSimilarity is: (e+z)/(e+2*u+z) With the above mentioned defintions the KulczynskiSimilarity is: e/u With the above mentioned defintions the JaccardSimilarity is: e/(e+u) With the above mentioned defintions the DiceSimilarity is: 2*e/(2*e+u) Z: number of Attribute for which both Examples have zero valuesĭistance of two values is 0 if both values are the same and 1 otherwise. U: number of Attribute for which both Examples have not equal values In case of using a similarity as a distance measure, the actual distance is calculated as the negative similarity.įor the different similarities the following variables are defined:Į: number of Attribute for which both Examples have equal and non-zero values

#Nn models sets how to#

This parameters defines how to calculate distances for only nominal Attributes in the input ExampleSet, in case the measure type is set to nominal measure. This parameter is available when the measure type parameter is set to 'mixed measures'. The only available option for mixed measure is the 'Mixed Euclidean Distance'.įor numerical values the euclidean distance is calculated.įor nomimal values, a distance of 0 is taken if both values are the same and a distance of one is taken otherwise. In case of only numerical Attributes different distance metrices can be used to calculate distances on this numerical Attributes.īregmann divergences are more generic 'closeness' measure types with does not satisfy the triangle inequality or symmetry.įor more details see the parameter divergence. In case of only nominal Attributes different distance metrices can be used to calculate distances on this nominal Attributes. Mixed Measures are used to calculate distances in case of both nominal and numerical Attributes. This parameter is used for selecting the type of measure to be used for finding the nearest neighbors. It can be useful to weight the contributions of the neighbors, so that nearer neighbors contribute more than more distant ones. If this parameter is set, the distance values between the Examples are also taken into account for the prediction.

K is typically a small, positive and odd integer. If k = 1, the Example is simply assigned to the class of its nearest neighbor. The ExampleSet that was given as input is passed through without changes.įinding the k training Examples that are closest to the unknown Example is the first step of the k-NN algorithm. This model can now be applied on unseen data sets for prediction of the label Attribute. The k-Nearest Neighbor model is delivered from this output port. It can be considered as the equivalent of the k-NN algorithm for unsupervised learning.Ī cluster calculated by the k-Means algorithm consists of similar Examples, where the similarity is calculated by a given measure in the Attribute space. This Operator performs clustering on an unlabeled data set. It can be useful to weight the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. In case of a regression, the predicted value is the average of the values of the found neighbors. In the second step, the k-Nearest Neighbor algorithm classify the unknown Example by a majority vote of the found neighbors.

The metric used and its exact configuration are defined by the parameters of the Operator. 'Closeness' is defined in terms of a distance in the n-dimensional space, defined by the n Attributes in the training ExampleSet.ĭifferent metrices, such as the Euclidean distance, can be used to calculate the distance between the unknown Example and the training Examples.ĭue to the fact that distances often depends on absolute values, it is recommended to normalize data before training and applying the k-Nearest Neighbor algorithm. The first step of the application of the k-Nearest Neighbor algorithm on a new Example is to find the k closest training Examples. The k-Nearest Neighbor algorithm is based on comparing an unknown Example with the k training Examples which are the nearest neighbors of the unknown Example.

This Operator generates a k-Nearest Neighbor model, which is used for classification or regression.