Pycaret: A Python Library for Machine Learning

What is PyCaret?

 

PyCaret is Python's open-source machine learning library that helps you prepare your data and deploy your models. It's easy to use, and a single line of code can perform almost any data science project task. With PyCaret, you can switch from preparing your data to serving your model from the notebook environment of your choice in seconds. 

 

PyCaret is simple and easy to use. All operations performed by PyCaret are sequentially stored in a  fully tuned pipeline for deployment. PyCaret automates all of this, including missing data assignments, categorical data transformations, feature engineering, and even hyperparameter tuning. 

 

PyCaret is inspired by the new role of Gartner's first term, Citizen Data Scientists. Citizen Data Scientists are power users who can perform both simple and moderately demanding analytical tasks that required more technical know-how.

 

It's an end-to-end machine learning and model management tool that dramatically speeds up your experimental cycle and increases your productivity. Compared to other open-source machine learning libraries, PyCaret is an alternative low-code library that can replace hundreds of lines of code with just a few lines. This makes the experiment dramatically faster and more efficient. 

 

PyCaret is a Python wrapper that surrounds several machine learning libraries and frameworks such as Scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, and Ray. 

 

Why should you use PyCaret?

 

1. It's a free and open-source program. Anyone interested in using it can do so.

2. Python is used to create it. This programming language is well-known among developers.

3. It is quick. Developers can deploy complicated models in a matter of minutes.

4. It's a low-code machine learning library. Developers are more productive because they spend less time coding.

5. It's a Python wrapper for existing Python modules like scikit-learn. As a result, there is no need for a distinct learning curve.

6. It works in tandem with other Python environments like PyCharm. PyCaret is simple to integrate with current machine learning workflows.

7. It is appropriate for both students and expert programmers.

 

Features of PyCaret

 

PyCaret is known for its ease of use. PyCaret is incredibly adaptable, has a single API, and has no learning curve when compared to other automated machine learning software.

 

PyCaret is jam-packed with features. Within a few lines of code, you can go from processing your data to training models and then deploying them on the cloud. It includes several pretreatment adjustments that are applied automatically once the experiment is started. PyCaret has approximately 70 untrained models for supervised and unsupervised tasks in its model zoo.

 

These are some of the best features of Pycaret:

 

1. Preparation of data

2. Training as a role model.

3. Tuning hyperparameters.

4. Interpretability and analysis

5. Models are chosen.

6. Logging your experiments is a good idea.

 

PyCaret can also be used on a GPU to speed up your workflow by a factor of ten. Simply specify use GPU-True in the setup function to train models on GPU. There has been no change in the code.

 

PyCaret is a solution that uses a glass box. It has a lot of features that allow you to interact with the model and examine its performance and results. 

 

For all models, conventional graphs such as the confusion matrix, AUC, residuals, and feature significance are accessible. It also works with the SHAP library, which may be used to explain the results of any sophisticated tree-based machine learning model.

 

(Also read: No-code machine learning platforms)

 

 

Getting started:

 

1. Installing PyCaret on a Computer:

 

It's about as simple as it gets. You can use pip to install the first stable version of PyCaret, v1.0.0. To get started, simply run the command below in your Jupyter Notebook:

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To install the full version:

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You should now have the option to change your environment to the one you just created after launching a Jupyter Notebook in your browser.

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2. Creating a PyCaret Environment:

 

PyCaret's setup function sets up the environment and establishes the modeling and deployment transformation pipeline. Before calling any other pycaret function, the setup must be called. Only one parameter is required: a pandas data frame. The preprocessing pipeline can be customized using the other parameters, which are all optional.

 

PyCaret's inference method will automatically infer the data types for all features based on specific properties when setup is run. Although the data type should be appropriately inferred, this is not always the case. 

 

PyCaret handles this by displaying a prompt when you complete the setup, asking for data type confirmation. If all data types are correct, press enter; otherwise, type quit to exit the setup.

 

In PyCaret, guaranteeing that the data types are right is critical since it conducts several type-specific preprocessing activities that are critical for machine learning models. To pre-define the data types, you can use the numeric_features and categorical_features options in the setup.

 

 

Parameterizing the Model:

 

We can start PyCaret's setup function now that we know what type of algorithm we're using. Data type inference, data cleaning and preprocessing, data sampling, training-test split, and assigning a session ID for repeatability are all included in this function.

 

1. Inference of Data Type:

 

You can use the setup tool to incorporate your data and target, which is the most basic part of data science and machine learning algorithms. Categorical, numeric, and your label are examples of data kinds. Once you've completed your setup, simply hit enter to check and confirm the data types of your columns/features.

 

2. Cleaning and preprocessing of data:

 

In this step, missing value imputation and categorical encoding are both applied automatically. For numeric features, missing values are filled with the mean value, and missing values for categorical features are filled with the feature's mode.

 

• further preprocessing includes ordinal and cardinal encoding, balancing, normalization, and transformation routines.

• There's target transformation, feature interaction group features, binning numeric features, and combining rare level functions for feature engineering.

 

3. Sampling Data:

 

This is a feature that is not found in other libraries that seek to improve data science methods. If the sample size is greater than 25,000, a linear model will be built to show how the sample size influences the model's performance.

 

4. Split your time between training and testing:

 

PyCaret includes a common function in its setup stage. A 70:30 split is the default ratio for dividing.

 

5. Session ID:

 

This feature is also known as a random seed or, in some cases, a random state. It is supplied so that your findings can be replicated in a different context or if you work on this topic again in the future.

 

That's it for getting started with PyCaret in a nutshell. PyCaret is a powerful competitor to scikit-learn, and we do not doubt that, like TensorFlow and pandas, it will become one of the most widely used libraries. Feel free to use your custom dataset to develop your ML classification model.

 

(Must Read: A Beginner’s Tutorial on PyTorch )