The algorithm enables a “backward propagation” over the respective neurons to make them more appropriately perceptive for the problem at hand (the essential functionality of that particular neural network for the requisite problem-solving). The data is run through a number of neurons over a number of different layers (to process different aspects of the data), with subsequent layers dependent on activations in the prior ones. There has to be a target variable that will be predicted. The data is in a basic spreadsheet and / or general dataframe structure, with variables in the column headers, row data as examples, and the information cells as numeric values (including for dummy and for categorical values). RapidMiner Studio is a powerful visual programming environment for rapidly building complete predictive analytic workflows. Basically, variables as columnar data is fed into the ANN, and based on observed features, the artificial neural network will reduce the data to particular outcomes. See rapidminer-studio-modular for the latest version 9.8+ - rapidminer-studio/GeneratePredictionRankingOperator.java.

In order to apply linear regression to a dataset and evaluate how well the model will perform, we can build a predictive learning process in RapidMiner Studio to predict a quantitative value. The “neurons” are represented by the round nodes, and the “synaptic signals” are represented by the lines (paths for the synaptic signaling). Outdated version of RapidMiner Studio 7.x - 9.7. The Linear Model Write the model to a file Linear regression is a simple while practical model for making predictions in many fields. The parameters of each supervised learning algorithm have been tuned to. Based on this basic approach, many types of ANNs have been created. The prediction methodology has been implemented by means of Rapidminer Studio 14.

Rapidminer studio prediction