Create R Model

# Create R Model# This experiment demonstrates how to use the **Create R Model** module to train, and score a naive bayes classification model using the breast cancer dataset, and use **Execute Python Script** to calculate performance and plot the performance curve. Similar to **Execute R Script** module, the **Create R Model** module allows a user to train a model using R scripts. In addition, it also allows the user to save the trained model similar to how other built-in machine learning models can be saved. This function enables the user to apply the trained model (by using R scripts) into the scoring experiment. ## Data ## We use "Breast cancer data", which is one of three cancer-related datasets provided by the Oncology Institute and can be found from UCI data repository. We use this data to classify the two types of cancer based on 9 attributes present in the data.  ## Experiment ## ### Train and Score the Model ### **Create R Model** module can be used if the user wants to train a model available in a R package. The user needs to provide two scripts, "Trainer R script" (used for training the model) and "Scorer R script" (used for scoring the model). In the "Trainer R script" box, the label and feature columns can be extracted from the input dataset using get.label.column() and get.feature.columns() functions. In this sample experiment, we train a binary classifier using Naive Bayes classification method. In the "Scorer R script" box, the user should generate a data frame with predicted class labels with corresponding probabilities on the input dataset using the input model.  After the **Create R Model** module is configured, it can be connected to **Train Model** module and then **Score Model** module as shown in the experiment graph. The user can save the trained model the same way as the models trained by other built-in machine learning modules. The following snapshot shows how to save the trained model. The **Score Model** module generates the scored dataset by including the predicted class labels and the corresponding predicted probabilities.  ### Evaluate the Model ### In this experiment, we evaluate the model by providing a custom Python script in the **Execute Python Script** module. This module plots the ROC curve and output the performance statistics. The Python scripts are shown in the snapshot below.  The output ROC curve and the evaluation metrics (accuracy, precision, recall, AUC) are shown in the following snapshots.