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A PMML scoring library in Scala
/*
* Copyright (c) 2017-2019 AutoDeploy AI
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.pmml4s
/**
* At various places the mining models use simple functions in order to map user data to values that are easier to use
* in the specific model. For example, neural networks internally work with numbers, usually in the range from 0 to 1.
* Numeric input data are mapped to the range [0..1], and categorical fields are mapped to series of 0/1 indicators.
*
* PMML defines various kinds of simple data transformations:
*
* - Normalization: map values to numbers, the input can be continuous or discrete.
* - Discretization: map continuous values to discrete values.
* - Value mapping: map discrete values to discrete values.
* - Text Indexing: derive a frequency-based value for a given term.
* - Functions: derive a value by applying a function to one or more parameters
* - Aggregation: summarize or collect groups of values, e.g., compute average.
* - Lag: use a previous value of the given input field.
*/
package object transformations {
}