PyCaret is an open-source, low-code machine learning library in Python, while caret is an R package that provides tools for predictive modeling. PyCaret is for Python users, and caret is for R users.
Intro to Python machine learning with PyCaret
10 mins read
Oct 31, 2025
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Python and machine learning are two prevalent topics among veteran and beginner developers alike. PyCaret is a relatively new Python library that represents a beautiful coupling of the two topics. There has been a boom in data in the past couple of decades. User activity is expanding rapidly along with the internet, creating massive amounts of information every day. This boom is referred to as “big data”, and it means that data scientists need a way to learn from all this useful information without drowning in it.
Data scientists in today’s environment require a faster and less complex method to experiment with data, which is a major reason why machine learning is so heavily used by data scientists today. To keep pace with these evolving demands, it’s essential for professionals in the field to continually learn machine learning techniques and stay updated with the latest advancements. Let’s explore the attributes of PyCaret, and how you can use it for machine learning with Python!
Machine learning overview#
Machine learning uses statistical functions and algorithms that allow models to make particular predictions and decisions. Machine learning uses algorithms to organize data, learn from that data, and utilize those learnings to make intelligent decisions and classifications without the direct input of the developer. This is the aim of machine learning models: to optimize computers to perform tasks without the need for human interaction or specific programming. This practice optimizes the functionality and overall efficiency of the computer.
Data analysis and data preparation, for example, become much more manageable when a computer performs the groundwork. All sci-fi references aside, machine learning is literally the practice of giving a functioning “brain” to our computers so that they can imitate how we grow and learn.
Machine learning is primarily used by data scientists to prepare and analyze a massive amount of data. This allows a data scientist to reach key insights in a fraction of the time it would take to evaluate all that data manually. Machine learning allows the computer to learn and adapt based on this constant stream of data, all without our help. There are three main types of machine learning:
-
- Includes clustering (market segmentation) and [anomaly detection]
- Helps to discover hidden trends and structures in our data
-
Supervised learning:
- Creates predictive models based on the training dataset (initial dataset)
- Includes regression and classification
-
Reinforcement learning:
- Aims to create intelligence in a system so that it may interact with the surrounding environment (e.g., self-driving cars)
- Is not supported by PyCaret
- Is supported by Python libraries like Tensorforce and Keras-RL
Machine learning models can be trained to find solutions using data patterns to deal with problems too complex for humans to develop an algorithm for. You can thank machine learning algorithms if you’ve experienced any of these moments:
- LinkedIn knowing exactly whom to suggest as a potential connection
- Music services knowing what new music you’d enjoy
- GPS services being able to accurately predict traffic
- A search engine knowing which websites are most relevant for your question
Get hands-on with Machine learning today.#
Try one of our 300+ courses and learning paths: Become a Machine Learning Engineer.
What is PyCaret?#
PyCaret is one of several Python libraries created for machine learning. (Others include NumPy, Keras, and Pandas. It is this vast collection of libraries and modules that have distinguished Python as a favorite among data scientists. PyCaret was inspired by the popular Caret package of R and joins the other renowned modules of Python. Caret is an acronym that stands for Classification And REgression Training. The acronym refers to both libraries’ ability to automate the machine learning pipelines for classification and regression problems. PyCaret comes with a set of modules that contain a variety of functions for specific machine learning tasks. A dataset that contains a classification problem will primarily use the classification module. There are also PyCaret modules for unsupervised learning, including anomaly detection, clustering, and natural language processing.
Each module houses specific algorithms for each distinction of machine learning while still recognizing universally used functions. For example, the create_model function will train and evaluate models in all PyCaret modules.
PyCaret is an open-source and low-code machine learning library. Being “low-code” refers to the automation of certain aspects of the development process, therefore reducing dependencies on the usual process of hand-coding. Low-code modules make it easier for those without specific training to participate in machine learning tasks. With low-code platforms, inexperienced employees can take more ownership and control over projects and produce required results. Even if you’re a seasoned developer, you can use low-code tools to accomplish more in far less time.
PyCaret also seeks to bypass some of the tedious processes of machine learning through automations. Some PyCaret automations that can be performed with a simple command include:
- Analyzing and comparing standard models
- Automatic model hyperparameters tuning
- Data transformation (converting raw data sets into usable formats)
- Model selection
- Training models
- Experiment logging
PyCaret is a Python wrapper that is built on other machine learning libraries and frameworks such as Scikit-learn, LightGBM, Catboost, and XGBoost. Because PyCaret works seamlessly with existing modules and programs, there is no steep learning curve to conquer. This also means that you can transport work done with PyCaret between multiple frameworks and libraries. In addition, PyCaret’s single API flattens the learning curve further and makes communication even more seamless.
Why use PyCaret for machine learning?#
This question doesn’t require too much analysis. Why wouldn’t you want to replace hundreds of lines of code with a few? If machine learning is already considered a champion sprinter in the world of data science, then PyCaret can speed up building machine learning projects even more. Not only is it faster, but simpler too. PyCaret provides a tremendous step forward in making the big data capabilities of machine learning more accessible.
PyCaret was designed with the “citizen data scientist” in mind. PyCaret simplifies the machine learning process so that someone who isn’t a highly skilled data scientist can handle sophisticated analytical tasks. Due to the rising dependence on machine learning across many industries, skilled data scientists are becoming increasingly scarce as they get scooped up by competing companies. But with tools like PyCaret, business analysts need no longer rely on the small expert community to get the predictive analysis they need.
Get hands-on with Python Machine Learning today.#
Try one of our 300+ courses and learning paths: A Practical Guide to Machine Learning with Python.
If you’re a beginner looking to get into machine learning, this is obviously great news. If you’re a skilled data scientist, then this is still great news. Being able to hire from a bigger pool of people who can work with datasets will boost your productivity as a team leader. Making advanced technical skills and expertise available to everybody is something that we at Educative and PyCaret have in common, it seems.
PyCaret can obviously handle essential data science functions, such as data visualization, as well as machine learning algorithms and models. But what specifically can you do with PyCaret today? As with many Python libraries, plenty of interesting projects are out there just waiting for contributors. For instance, take a look at the FIFA Player Market Value Predictions and Wine Quality Dataset projects on GitHub. After a little practice, you could jump into projects like these to refine your PyCaret and Python machine learning skills!
If you’re an emerging data scientist looking to make your mark, then Kaggle competitions are a great place to start. Kaggle hosts a vast collection of machine learning competitions with a diverse range of topics and datasets to work with. No matter where you are in your machine learning journey, Kaggle hosts a competition that is a great fit for your skillset! Checking your model’s accuracy on the leaderboard is a convenient way to compare your machine learning abilities against your peers. Reaching the top of that competitive leaderboard is also a great chance to earn some bragging rights amongst the machine learning and data science community.
What’s new in PyCaret 3.x and breaking changes#
PyCaret has evolved significantly since its earlier versions. Version 3 introduces changes that every user should know before following older tutorials:
Python and dependency support: PyCaret 3.x supports Python 3.9+ and drops compatibility for older versions (like Python 3.8).
API improvements and modularization: The library now offers both functional and OOP experiment APIs, with clearer modular boundaries (for example, classification and regression modules are now separated).
Compatibility with the latest libraries: PyCaret now supports the latest pandas, scikit-learn, and other dependencies. Be aware that some code from PyCaret 2.x may require updates to run.
Deprecated functionality: Some older utility functions or arguments might be phased out; always check the changelog or migration notes.
Time series forecasting with PyCaret#
PyCaret now includes a dedicated time series / forecasting module, making it possible to build predictive models for sequential data with minimal code.
Quick example (univariate):#
from pycaret.time_series import TSForecastingExperimentexp = TSForecastingExperiment()exp.setup(data=your_series, fh=12, fold=3, session_id=42)best = exp.compare_models()future = exp.predict_model(best)
This module supports feature engineering, model comparison, hyperparameter tuning, and forecasting horizons (fh). It combines classical forecasting techniques and ML-based models under one interface.
Enabling GPU acceleration in PyCaret#
To speed up training, PyCaret supports GPU acceleration (for some estimators) through RAPIDS / cuML integration.
Use
use_gpu=Truein yoursetup()call (or as part of estimator parameters).Only selected models support GPU acceleration.
You may need GPU-compatible versions of LightGBM, XGBoost, or cuML installed.
Results can be 2x–200x faster, depending on workload.
This feature is especially valuable for larger datasets or iterative experiments.
MLOps & experiment tracking (MLflow integration)#
PyCaret includes built-in support for experiment logging via MLflow, making it easier to manage models and track results.
Use
log_experiment=Truewithexperiment_nameinsetup()to enable logging.It logs parameters, metrics, model artifacts, and plots automatically.
Time series experiments also support MLflow logging.
This capability makes your workflows reproducible, auditable, and production-ready.
Installation options and extras (slim, full, GPU, etc.)#
Not all PyCaret installations are the same — knowing which extras to include can save you setup headaches.
pip install pycaret # installs the slim version (core dependencies only)pip install pycaret[full] # installs all optional dependencies
GPU or MLflow support may require additional libraries (like
cuMLor GPU-enabledLightGBM).Including this information ensures newcomers install the right variant from the start, avoiding missing imports or version conflicts.
When (and when not) to use PyCaret vs other AutoML tools#
PyCaret is powerful, but it’s not the best solution for every use case. Here’s a quick guide:
Best use cases:#
Rapid prototyping and benchmarking across many models
Low-code workflows where you don’t want to build full pipelines
Forecasting, classification, or clustering tasks with standard requirements
Limitations / when to avoid:#
Custom architectures or complex pipelines requiring fine-grained control
Deep learning or neural network–heavy workflows
Very large datasets requiring distributed computing
This comparison helps you make informed decisions based on project needs.
Limitations and best practices to avoid pitfalls#
No tool is perfect, and PyCaret is no exception. Here are some best practices to keep in mind:
Data leakage risk — Ensure target leakage isn’t introduced, especially with automatic feature engineering.
Overfitting risk — Monitor cross-validation metrics carefully; don’t rely solely on leaderboard results.
Unsupported tasks — PyCaret doesn’t support reinforcement learning or custom deep-learning architectures.
Scalability constraints — For extremely large datasets, switch to scikit-learn pipelines or custom solutions.
Following these tips will help you get the most out of PyCaret while avoiding common mistakes.
Wrapping up and next steps#
Machine learning is complex by nature, so it’s refreshing to work with a Python library designed to expand the field to so many more people. Eager to give PyCaret a shot? Downloading PyCaret is as easy as typing the command pip install pycaret [full].
Even with PyCaret, breaking into the field of machine learning still requires plenty of training. Be sure to check out our Simplifying Machine Learning with PyCaret in Python course if you want to get started with PyCaret!
Happy learning!
Continue learning about Python and machine learning#
· Deep learning vs. Machine learning
· 7 top Python libraries for data science and machine learning
Frequently Asked Questions
What is the difference between PyCaret and Caret?
What is the difference between PyCaret and Caret?
Written By:
Hunter Johnson
