Generate Weight (LPR)


Synopsis

This operator uses the distance between an example's label value and the result of a local polynomial regression to determine the weight of this example.


Description

This operator performs a weighting of the examples and hence the resulting exampleset will contain a new weight attribute. If a weight attribute was already included in the exampleSet, its values will be used as initial values for this algorithm. If not, each example is assigned a weight of 1.

For calculating the weights, this operator will perform a local polynomial regression for each example. For more information about local polynomial regression, take a look at the operator description of the local polynomial regression operator Local Polynomial Regression.

After the predicted result has been calculated, the residuals are computed and rescaled using their median.

This result will be transformed by a smooth function, which cuts of values greater than a threshold. This means, that examples without prediction error will gain a weight of 1, while examples with an error greater than the threshold will be down weighted to 0.

This procedure is iterated as often as specified by the user and will result in weights, which will penalize outliers heavily. This is especially useful for algorithms using the least squares optimization such as Linear Regression, Polynomial Regression or Local Polynomial Regression, since least square is very sensitive to outliers.


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ExampleProcess