04_Classification_and_Predictive_Modelling
This directory contains 11 workflows.
01_Example_for_Learning_a_Decision_Tree
After the data is partitioned into train and test set, a decision tree model is trained and applied.
02_Example_for_Learning_a_Neural_Network
After the data is normalized and partitioned, Multi-Layer-Perzeptron (MLP) is trained and applied.
03_Example_for_Learning_a_Naive_Bayes_Model
A simple example using a Naive Bayes learner and predictor to classify some shuttle data. For more background information see" […]
04_Exporting_a_Decision_Tree_as_Image
The workflow learns a decision tree on a data set and applies the model on a new data set, whereby the distribution is shown in small histogram depiction.
05_Gradient_Boosted_Trees
This workflow shows how to learn a Gradient Boosted Trees model on the adult data set.
06_Logistic_Regression
This workflow is an example of how to build a basic prediction / classification model using logistic regression.
07_Decision_Tree
This workflow is an example of how to build a basic prediction / classification model using a decision tree. Dataset describes wine chemical features. […]
08_Regularized_Logistic_Regression
The goal of this workflow is to analyze the impact of different priors in case of the logistic regression. The workflow therefore first reads the internet […]
09_Random_Forest
Training a decision tree and training a random forest of decision trees.
10_Analyzing_Churn_Models_with_the_Binary_Classification_Inspector
This workflow demonstrates the functionality of the Binary Classification Inspector node. It produces a complex view made of four different charts in order […]