PyCaret: An Open-Source Framework for Simplifying Machine Learning Workflows

Positioning PyCaret in the ML Ecosystem

Core Experiment Lifecycle

Preprocessing as a First-Class Feature

Building and Comparing Models with PyCaret

Binary Classification Workflow

Regression with Custom Metrics

Time Series Forecasting

Clustering

Classification Models Supported in the Built-In Model Library

Regression Models Supported in the Built-In Model Library

Time Series Forecasting Models Supported in the Built-In Model Library

Beyond the Built-In Library: Custom Estimators, MLOps Hooks, and Removed Modules

Conclusion

Frequently Asked Questions

Unlocking the Power of Machine Learning with PyCaret: A Deep Dive

In the ever-evolving landscape of data science, there’s an increasing demand for tools that streamline the machine learning (ML) process without sacrificing control or flexibility. Enter PyCaret—an open-source, low-code machine learning library designed to simplify and standardize the end-to-end machine learning workflow.

What is PyCaret?

PyCaret isn’t just another AutoML solution; it acts as an experiment orchestration layer that wraps many popular machine learning libraries under a consistent API. While some AutoML tools focus heavily on automating decision-making, PyCaret emphasizes accelerating repetitive tasks like preprocessing, model comparison, tuning, and deployment. This unique approach makes workflows more transparent and controllable, providing a solid balance between automation and user input.

Positioning PyCaret in the ML Ecosystem

Unlike traditional AutoML engines that conduct exhaustive model and hyperparameter searches, PyCaret aims to reduce human effort and minimize boilerplate code. It aligns well with the “citizen data scientist” concept, which emphasizes productivity while maintaining standardization in data workflows. Drawing inspiration from the R caret library, PyCaret emphasizes consistency across various model families.

Core Experiment Lifecycle

Across different tasks, including classification, regression, time series, clustering, and anomaly detection, PyCaret enforces a consistent lifecycle:

1. setup(): Initializes the experiment and builds the preprocessing pipeline.
2. compare_models(): Benchmarks models using cross-validation.
3. create_model(): Trains a specific ML estimator.
4. Optional tuning or ensembling: Enhances model performance as needed.
5. finalize_model(): Retrains the selected model on the full dataset.
6. predict_model(), save_model(), or deploy_model(): Handles inference or deployment processes.

This separation between evaluation and finalization is crucial for robust model assessment.

Preprocessing as a First-Class Feature

PyCaret treats preprocessing as an integral part of the model-building process. All transformations—like imputation, encoding, and scaling—are encapsulated within a single pipeline object. This ensures that preprocessing steps are reused during inference and deployment, reducing the risk of discrepancies between training and serving environments.

Building and Comparing Models with PyCaret

PyCaret’s intuitive design allows users to easily build and compare models. Below is a binary classification workflow example using an example dataset called "juice".

from pycaret.datasets import get_data
from pycaret.classification import *

# Load example dataset
data = get_data("juice")

# Initialize experiment
exp = setup(data=data, target="Purchase", session_id=42, normalize=True, remove_multicollinearity=True, log_experiment=True)

# Compare all available models
best_model = compare_models()

# Inspect performance on holdout data
holdout_preds = predict_model(best_model)

# Train final model on full dataset
final_model = finalize_model(best_model)

# Save pipeline + model
save_model(final_model, "juice_purchase_model")

This concise code illustrates how setup() builds a comprehensive preprocessing pipeline, compare_models() benchmarks various algorithms effortlessly, and finalize_model() ensures the final model is trained on the complete dataset.

Regression, Time Series, and Clustering Workflows

PyCaret extends its capabilities to various types of ML problems. For instance, in a regression task with custom metrics, PyCaret allows for quick comparisons while enabling tuning.

For time series forecasting, PyCaret adapts its workflow without compromising familiarity. Here’s an example using the "airline" dataset:

from pycaret.datasets import get_data
from pycaret.time_series import *

y = get_data("airline")

# Initialize experiment
exp = setup(data=y, fh=12, session_id=7)

# Compare models
best = compare_models()
forecast = predict_model(best)

In clustering tasks, PyCaret simplifies the process too:

from pycaret.clustering import *
from pycaret.anomaly import *

# Clustering
exp_clust = setup(data, normalize=True)
kmeans = create_model("kmeans")
clusters = assign_model(kmeans)

Comprehensive Model Libraries

PyCaret comes equipped with a diverse set of built-in models across different ML tasks. For classification, the library includes models like Logistic Regression, Random Forest, XGBoost, and many more. The same applies to regression and time series forecasting, making it a versatile tool for any data scientist.

Beyond the Built-in Library

PyCaret’s flexibility extends beyond its built-in models. Users can integrate custom estimators if they follow the scikit-learn API. Additionally, PyCaret offers experiment tracking hooks for integration with tools like MLflow, enabling smooth deployment workflows in various cloud environments.

Conclusion

PyCaret serves as a unified framework that harmonizes the machine learning journey, making it accessible without sacrificing control or flexibility. By treating preprocessing as part of the model and providing a consistent lifecycle, it encourages productive, informed experimentation. Whether you are a novice data scientist or seasoned professional, PyCaret balances productivity with control, making it an ideal choice for rapid experimentation and serious production-oriented workflows.

Frequently Asked Questions

Q1. What is PyCaret and how is it different from traditional AutoML?
A: PyCaret is an experiment framework that standardizes ML workflows and reduces boilerplate, while keeping preprocessing, model comparison, and tuning transparent and user-controlled.

Q2. What is the typical workflow in a PyCaret experiment?
A: A PyCaret experiment typically follows the lifecycle of setup, model comparison, training, optional tuning, finalization on full data, and then prediction or deployment.

Q3. Can PyCaret use custom models outside its built-in library?
A: Yes, any scikit-learn compatible estimator can be integrated into the same training, evaluation, and deployment pipeline alongside built-in models.

Hello! I’m Janvi, a passionate data science enthusiast currently working at Analytics Vidhya. My journey into the world of data began with a deep curiosity about how we can extract meaningful insights from complex datasets.