Iris classification

This page contains an example of using the MLSToolbox Code Generator tool for graphically defining an ML pipeline and generating the corresponding Python code. The defined ML pipeline aims to train two models for predicting the species of iris flowers using the Random Forest Classifier and SVC algorithms. The selected dataset for this example is the Iris dataset.

Dataset information

The dataset is composed of 160 samples and 6 columns. The first variables describe flower characteristics (sepal lenght (cm), sepal width (cm), petal length (cm), petal width (cm)), while the two last variable represents the target code representing the species and the corresponding name.

The pipeline

The pipeline is composed of the following stages:

• Data Collection: gets the iris data from the iris dataset
• Split: splits the features data into features_train and features_test data and truth data into truth_train and truth_test data.
• Model Training: features_train and truth_train data are used to train a model using and the Random Forest Classifier algorithm and the same data is used to train another model using the SVC algorithm.
• Model Evaluation: calculates the accuracy metric of both trained models.

All the information of the Iris classification example and the generated code of the ML pipeline can be found on the pipeline examples repository.