An Easy Introduction to Scikit-learn: An All-Inclusive Handbook on the Intel Extension and Library.
What is Scikit learn?
Free and open-source Python machine learning library scikit-learn (previously scikits.learn and sklearn). It supports support-vector machines, random forests, gradient boosting, k-means, and DBSCAN for classification, regression, and clustering and works with NumPy and SciPy. Scikit-learn is NumFOCUS-funded.
How does Scikit-learn work?
The NumPy, SciPy, and matplotlib libraries serve as the foundation for the well-known Python machine learning program Scikit-learn, better known as sklearn. With support vector machines, random forests, gradient boosting, and k-means among its many classification, regression, and clustering algorithms, it is an easy-to-use and effective tool for data mining and analysis.
Scikit-learn may be applied to both supervised and unsupervised machine learning techniques, and it provides a great degree of flexibility in fine-tuning models. The availability of several methods and its easy-to-use interface make this module one of the most popular traditional machine learning frameworks. Over the years, the scikit-learn project has continued to develop with a number of new features and releases.
For single-node and multi-node setups, Intel offers the Intel Extension for Scikit-learn to expedite scikit-learn workflows or applications for Intel architectures.
This blog explains the Intel extension, walks you through the code step-by-step, and outlines the performance advantages of utilising it.
Pros and Cons of Scikit learn
A free Python library called Scikit-learn offers data mining and machine learning tools and methods. Scikit-learn has the following benefits and drawbacks:
Pros
Simple to use Scikit-learn works well with small to medium-sized datasets and is simple to learn and use.
Integrated algorithms Numerous built-in algorithms for common machine learning applications, including clustering, regression, and classification, are available in Scikit-learn.
The Python scientific and numerical libraries NumPy and SciPy are compatible with Interoperable Scikit-learn.
Cons
Limited assistance for deep learning There are no built-in deep learning features in Scikit-learn. You might need to combine Scikit-learn with libraries like TensorFlow or Keras in order to manage complex neural network jobs.
Large or complicated datasets might not be a good fit. Because of its limited deep learning capability, Scikit-learn might not be the best tool for managing complicated or sizable datasets.
Scikit Learn Capabilities
The following are some of Scikit-learn’s features:
Analysing and mining data
Scikit-learn offers data mining and analysis tools, such as clustering, regression, and classification methods.
Integrated plotting
You may visualise model performance using the plotting API that comes with Scikit-learn. Plotting tools include the ROC curve, precision-recall curves, partial dependence plots, and confusion matrix.
Extraction of features
To give data to an ML model, Scikit-learn must be able to translate text and images into numerical values.
Automated feature selection
This function aids in locating and keeping your model’s most instructive elements, which can enhance its precision, comprehensibility, and effectiveness.
Hyperparameter optimization
Standard evaluations offered by Scikit-learn can aid in iterative improvement and offer information on model performance.
Measures of evaluation
Numerous assessment metrics and methods are supported by Scikit-learn for machine learning model validation and optimisation.
Open-source
Scikit-learn is available for commercial use under the BSD license and is open source.
Built on top of NumPy, SciPy, and matplotlib
NumPy, SciPy, and matplotlib are the foundations upon which Scikit-learn is based.
Intel Extension for Scikit-learn
Your scikit-learn applications may be easily scaled up using Intel’s addition, which also improves performance for a variety of ML techniques by dynamically patching scikit-learn estimators. You can utilise the machine learning tool with your current AI packages because it can be purchased alone or as part of AI Tools.
Features
- Increase the size of your scikit-learn algorithms by substituting accelerated, mathematically equivalent estimators for your current ones.
- Because the Intel oneAPI Data Analytics Library powers the accelerations, you can run on the Intel CPU or GPU of your choosing.
- Decide on the accelerations’ application method:
- With no modifications to the code, patch all compatible algorithms from the command line.
- In your Python script, add two lines of code to patch all compatible algorithms.
- Make sure your script only patches specific algorithms.
- For all scikit-learn purposes, patch and unpatch your environment globally.
Starting Out
Installation
The following methods are available for installing the extension:
- Among the AI Tools
- Independent version:
- Install using PyPI, which is by default advised.
- Utilise Anaconda Cloud to install
Patching
When scikit-learn is patched with the Intel Extension for Scikit-learn, the stock original estimators in the scikit-learn workflows are swapped out with the improved versions that the extension offers. Patching, to put it simply, is turning on the extension optimisations. If the extension does not support the specified algorithm parameters, the original scikit-learn result is returned.
Patching scikit-learn with the extension can be done in a few different ways:
- Using the following command line, you can use Scikit-Learn without changing its code. flag – python -m sklearnex my_application.py
- Straight from the script:
from sklearnex import patch_sklearn
patch_sklearn() - By importing the preferred estimator into your script from the sklearnex module:
from sklearnex.neighbors import NearestNeighbors - Using global patching to make your scikit-learn installation patchable for all future runs – python -m sklearnex.glob patch_sklearn
Users can also always reverse the patch. Reverting back to the original scikit-learn implementation and substituting the stock scikit-learn algorithms for the patched ones is the only way to reverse the modification. Reimporting scikit-learn is necessary after unpatching:
sklearnex.unpatch_sklearn()
Re-import scikit-learn algorithms after the unpatch:
from sklearn.cluster import KMeans
Sample Code
The support vector machine (SVM) classifier from the Intel Extension for Scikit-learn is demonstrated in this sample code for a digit recognition challenge. Users can also learn how to save data to a file and train a model. The usage of scikit-learn is comparable to that of all other machine learning techniques.
The Intel extension relies on daal4py, a streamlined API to the Intel oneAPI Data Analytics Library that enables quick use of the framework for those interested in machine learning or data science.
It will use the SVM ML classification technique in this code example to identify handwritten digits. Numbers written by hand It comes from the Sklearn Toy datasets. 1797 input photos are included in the Digits dataset. There are 64 pixels (8×8 matrix) in each image that serve as features. Ten classes in the output correspond to each of the digits (0–9).
The code sample incorporates the following steps:
- Bring all required packages in. In this case, the Intel Extension for Scikit-learn patches The Intel oneAPI Data Analytics Library is used as the underlying solution in scikit-learn estimators.
- Split the dataset into train and test after loading it.
- After training the model, save it to a file.
- Apply the trained model to test photos and predict the digit.
- Export the results to a CSV file and see how accurate the trained model is on test data.
Benefits of Performance
ML training and inference performance comparisons between Intel Extension for Scikit-learn and scikit-learn show the orders of magnitude acceleration that may be readily attained with the Intel extension.

Speedup with Intel Extension for Scikit-learn over the original package for FP32 (floating point 32) Workloads

Speedup with Intel Extension for Scikit-learn over the original package for FP64 (floating point 64) Workloads
Next Steps?
For your traditional machine learning operations, Scikit-learn is a straightforward and adaptable tool. Your applications’ performance can be significantly improved by the Intel Extension for Scikit-learn’s optimizations, which also lower the computational demands of AI workloads.