PyCaret: all about the open source Machine Learning library in Python

Building a machine learning model goes through several steps and can be very time consuming. PyCaret, an open source library, can help run all end-to-end Machine Learning processes more and faster with fewer lines of code.

Table of Contents

PyCaret: what is it?

PyCaret is a low-code open source library in python designed to automate the development of machine learning models. This library is intended for data scientists, Machine Learning engineers, but also for learners who want to be more productive and want to obtain conclusions more quickly.

In developing PyCaret, Moez Ali, its author, had a clear objective. He aspired to make machine learning understandable and accessible to everyonefor beginners and experts alike.

This is an end-to-end machine learning and model management tool that significantly speeds up the experimentation cycle and increases productivity. This solution supports supervised learning (classification and regression), clustering, anomaly detection and natural language processing tasks.

After creating the machine learning model, the author can directly deploy the transformation pipeline and trained model on Amazon Web Service (AWS), Microsoft Azure or Google Cloud Platform (GCP).

Why use PyCaret?

PyCaret has many advantages and as many reasons to adopt it.

  • PyCaret offers automation of crucial machine learning steps (defining data transformations, evaluating and comparing standard models, setting model hyperparameters …).
  • It is easy to use. The library helps to perform end-to-end ML implementations with less coding.
  • It is a wrapper for existing Python modules like scikit-learn. As a result, there is no need for a separate learning curve.
  • The library is ready to use. It allows rapid prototyping of models on environments of the company’s choice. The tool is now widely used by start-ups.
  • PyCaret can be used to implement the following machine learning models: classification, regression, clustering, anomaly detection, natural language processing, and rule mining.
  • It works in tandem with other Python environments such as PyCharm. PyCaret is simple to integrate with current machine learning workflows.
  • It is suitable for both students and expert programmers.

The different features of PyCaret

PyCaret is full of features. In a few lines of code, the tool allows to go from data processing to training modelsand then deploy them on the cloud.

The package contains more than 70 automated open source algorithms and nearly 25 preprocessing techniques. These features help to create machine learning models with performance. Here are some of the best features of PyCaret:

  • Data preparation
  • Model training
  • Setting of hyperparameters
  • Interpretability and analysis
  • The models are chosen
  • Recording your experiences is a good idea

PyCaret has many features that allow you to interact with the model and examine its performance as well as its results. For all models, conventional graphs such as confusion matrix, AUC, residuals, and feature significance are available.

It also works with the SHAP library. The latter can be used to explain the results of any sophisticated decision tree-based machine learning model.

How to create a PyCaret environment

PyCaret: all about the open source Machine Learning library in Python

The first step before starting a machine learning project in PyCaret is to configure the environment in two steps :

Importing a module

Depending on the type of problem to solve, importing the module is the first thing to do. In the first version of PyCaret, 6 different modules are available: regression, classification, clustering, natural language processing (NLP), anomaly detection and associated mining rule.

Initialization of the configuration

In this step, PyCaret performs some basic preprocessing tasks:

  • Ignore IDs and date columns
  • Imput missing values
  • Encode categorical variables
  • Divide the data set into test-train fractions for the rest of the modeling steps.

Running the setup function will first confirm the data types. By validating, the user creates the environment.

Training a machine learning model using PyCaret

This essentially involves two steps.

Form a model

Forming a model in PyCaret is quite simple. You just have to use the create_model function.

Setting hyperparameters

It is possible to adjust the hyperparameters of a machine learning model using the tune_model function. PyCaret offers a lot of flexibility. For example, the tool allows the number of folds to be set using the fold parameter in the tune_model function.

It is also possible to change the number of iterations by using the parameter n_iter . Increasing the n_iter parameter will obviously increase the training time, but will give much better performance.

Build ensemble models and compare them

PyCaret: all about the open source Machine Learning library in Python

Ensemble models in machine learning combine decisions from multiple models to improve overall performance. In PyCaret, the user can create bagging, boosting, blending and stacking ensemble models with a single line of code.

Comparing models is another useful feature of the PyCaret library. In order not to try different models one by one, the user can use the model comparison function.

This will form and compare common evaluation metrics for all models available in the library of the imported module. This function is only available in the pyCaret.classification and pyCaret.regression modules.

Analyze the model

After training the model, the next step is to analyze the results. Experts will say that this step is particularly useful from a business point of view. Analyzing a model in PyCaret is again very simple. With a single line of code, the user can :

Get a plot of the model results

Model performance analysis in PyCaret is done via plot_model. This allows plotting the decision limits, precision recall curve, validation curve, residual plots, etc.

In addition, for clustering models, the user can get the kink plot and the silhouette plot. For textual data, it is possible to plot word clouds, bigram and trigram frequency plots, etc.

Interpreting the results

Interpreting the model results helps debug the model by analyzing important features. This is a crucial step in industrial-level machine learning projects.

In PyCaret, the user can interpret the model by SHAP values and correlation diagram with a single line of code.

Plot the results of the model or evaluate it

PyCaret: all about the open source Machine Learning library in Python

PyCaret allows to plot the results of the model by providing the model object as a parameter and the desired plot type.

If the user does not want to plot all these visualizations individually, the library has another amazing feature: model evaluation (evaluation_model). In this function, just pass the model object and PyCaret will create an interactive window to view and analyze the model in all possible ways.

Interpret the model and make predictions

Interpreting complex models is very important in most machine learning projects. It helps to debug the model by analyzing what the model thinks is important.

In PyCaret, this step is as simple as writing interpret_model to get the Shapley values.

Finally, there are predictions on unpublished data. For this, we simply pass the model used for predictions and the dataset. It is necessary to make sure that all the elements are in the same format as the one provided when the environment was configured earlier.

PyCaret builds a pipeline of all the steps, will pass the unseen data through this channel and provide the results. Once the model has been built and tested, all that is left to do is save it in the pickle file using the save_model function.

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