Tuning your model using the python-based low-code machine-learning library PyCaret

Today, I’ll discuss another important topic before I will share the excellent use case next month, as I still need some time to finish that one. We’ll see how we can leverage the brilliant capability of a low-code machine-learning library named PyCaret.

But before going through the details, why don’t we view the demo & then go through it?

Architecture:

Let us understand the flow of events –

As one can see, the initial training requests are triggered from the PyCaret-driven training models. And the application can successfully process & identify the best models out of the other combinations.

Python Packages:

Following are the python packages that are necessary to develop this use case –

pip install pandas
pip install pycaret

PyCaret is dependent on a combination of other popular python packages. So, you need to install them successfully to run this package.

CODE:

• clsConfigClient.py (Main configuration file)

I’m skipping this section as it is self-explanatory.

• clsTrainModel.py (This is the main class that contains the core logic of low-code machine-learning library to evaluate the best model for your solutions.)

Let us understand the code in simple terms –

1. Import necessary libraries and load the Titanic dataset.
2. Initialize the PyCaret setup, specifying the target variable, train-test split, categorical and ordinal features, and features to ignore.
3. Compare various models to find the best-performing one.
4. Create a specific model (Random Forest in this case).
5. Perform hyper-parameter tuning on the Random Forest model.
6. Evaluate the model’s performance using a confusion matrix and AUC-ROC curve.
7. Finalize the model by training it on the complete dataset.
8. Make predictions on new data.
9. Save the trained model for future use.

• trainPYCARETModel.py (This is the main calling python script that will invoke the training class of PyCaret package.)

The above code is pretty self-explanatory as well.

• testPYCARETModel.py (This is the main calling python script that will invoke the testing script for PyCaret package.)

In this code, the application uses the stored model & then forecasts based on the optimized PyCaret model tuning.

Conclusion:

The above code demonstrates an end-to-end binary classification pipeline using the PyCaret library for the Titanic dataset. The goal is to predict whether a passenger survived based on the available features. Here are some conclusions you can draw from the code and data:

1. Ease of use: The code showcases how PyCaret simplifies the machine learning process, from data preprocessing to model training, evaluation, and deployment. With just a few lines of code, you can perform tasks that would require much more effort using lower-level libraries.
2. Model selection: The compare_models() function provides a quick and easy way to compare various machine learning algorithms and identify the best-performing one based on the chosen evaluation metric (accuracy by default). This selection helps you select a suitable model for the given problem.
3. Hyper-parameter tuning: The tune_model() function automates the process of hyper-parameter tuning to improve model performance. We tuned a Random Forest model to optimize its predictive power in the example.
4. Model evaluation: PyCaret provides several built-in visualization tools for assessing model performance. In the example, we used a confusion matrix and AUC-ROC curve to evaluate the performance of the tuned Random Forest model.
5. Model deployment: The example demonstrates how to make predictions using the trained model and save the model for future use. This deployment showcases how PyCaret can streamline the process of deploying a machine-learning model in a production environment.

It is important to note that the conclusions drawn from the code and data are specific to the Titanic dataset and the chosen features. Adjust the feature engineering, preprocessing, and model selection steps for different datasets or problems accordingly. However, the general workflow and benefits provided by PyCaret would remain the same.

So, finally, we’ve done it.

I know that this post is relatively bigger than my earlier post. But, I think, you can get all the details once you go through it.

You will get the complete codebase in the following GitHub link.

I’ll bring some more exciting topics in the coming days from the Python verse. Please share & subscribe to my post & let me know your feedback.

Till then, Happy Avenging! 🙂

Note: All the data & scenarios posted here are representational data & scenarios & available over the internet & for educational purposes only. Some of the images (except my photo) we’ve used are available over the net. We don’t claim ownership of these images. There is always room for improvement & especially in the prediction quality.