Dataset for the Tutorial

For this tutorial we will use the Iris Dataset from UCI. This is perhaps the best known database to be found in the pattern recognition literature. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. Short descriptions of each column are as follows:

• sepal_length: Length of Sepal
• sepal_width: Width of Sepal
• petal_length: Length of Petal
• petal_width: Width of Petal
• species: One of three class (Setosa, Versicolour, Virginica) Target Column

Dataset Acknowledgement:

Creator: R.A. Fisher, Donor : Michael Marshall (MARSHALL%PLU '@' io.arc.nasa.gov)

The original dataset and data dictionary can be found here.

Getting the Data

You can download the data from the original source found here and load it using pandas (Learn How) or you can use PyCaret's data respository to load the data using the get_data() function (This will require an internet connection).

In later tutorials we will learn how to overwrite PyCaret's infered data type using the numeric_features and categorical_features parameters in setup().

 Comparing All Models

Comparing all models to evaluate performance is the recommended starting point for modeling once the setup is completed (unless you exactly know what kind of model you need, which is often not the case). This function trains all models in the model library and scores them using stratified cross validation for metric evaluation. The output prints a score grid that shows average Accuracy, Recall, Precision, F1, Kappa, and MCC accross the folds (10 by default) along with training times.

Create a Model

create_model is the most granular function in PyCaret and is often the foundation behind most of the PyCaret functionalities. As the name suggests this function trains and evaluates a model using cross validation that can be set with fold parameter. The output prints a score grid that shows Accuracy, Recall, Precision, F1, Kappa and MCC by fold.

For the remaining part of this tutorial, we will work with the below models as our candidate models. The selections are for illustration purposes only and do not necessarily mean they are the top performing or ideal for this type of data.

• Decision Tree Classifier ('dt')
• K Neighbors Classifier ('knn')
• Logistic Regression ('lr')

There are 18 classifiers available in the model library of PyCaret. Please view the create_model() docstring for the list of all available models.

Decision Tree Classifier

 Neighbors Classifier

Logistic Regression

Tune a Model

When a model is created using the create_model() function it uses the default hyperparameters to train the model. In order to tune hyperparameters, the tune_model() function is used. This function automatically tunes the hyperparameters of a model using Random Grid Search on a pre-defined search space. The output prints a score grid that shows Accuracy, AUC, Recall, Precision, F1, Kappa, and MCC by fold for the best model. To use the custom search grid, you can pass custom_grid parameter in the tune_model function (see 9.2 KNN tuning below).

Decision Tree Classifier

K Neighbors Classifier

 Logistic Regression

Plot a Model

Before model finalization, the plot_model() function can be used to analyze the performance across different aspects such as AUC, confusion_matrix, decision boundary etc. This function takes a trained model object and returns a plot based on the test / hold-out set.

There are 15 different plots available, please see the plot_model() docstring for the list of available plots.

Confusion Matrix

Classification Report

Decision Boundary Plot

Prediction Error Plot