A machine learning (ML) library for the Python programming language, Scikit-learn has a large number of algorithms that can be readily deployed by programmers and data scientists in machine learning models.


What Is Scikit-learn?

Scikit-learn is a popular and robust machine learning library that has a vast assortment of algorithms, as well as tools for ML visualizations, preprocessing, model fitting, selection, and evaluation.  

Building on NumPy, SciPy, and matplotlib, Scikit-learn features a number of efficient algorithms for classification, regression, and clustering. These include support vector machines, rain forests, gradient boosting, k-means, and DBSCAN.  

Scikit-learn boasts relative ease-of-development owing to its consistent and efficiently designed APIs, extensive documentation for most algorithms, and numerous online tutorials.  

Current releases are available for popular platforms including Linux, MacOS, and Windows.

Why Scikit-learn?

The Scikit-learn API has become the de facto standard for machine learning implementations thanks to its relative ease of use, thoughtful design, and enthusiastic community.    

Scikit-learn provides the following modules for ML model building, fitting, and evaluation:  

  • Preprocessing refers to Scikit-learn tools useful in feature extraction and normalization during data analysis.
  • Classification refers to a set of tools that identify the category associated with data in a machine learning model. These tools can be used  to categorize email messages as either valid or as spam, for example. Essentially, classification  identifies to which category an object belongs.
  • Regression refers to the creation of an ML model that tries to understand the relationship between input and output data, such as the behavior or stock prices. Regression predicts a continuous-valued-attribute associated with an object.
  • Clustering tools in Scikit-learn automatically group data with similar characteristics into sets, such as customer data arranged in sets based on physical location.
  • Dimensionality reduction reduces the number of random variables for analysis. For example, to increase the efficiency of visualizations, outlying data may be left out.
  • Model selection refers to algorithms and their ability to offer tools that compare, validate, and select optimal parameters for use in data science machine learning projects.
  • Pipeline refers to utilities for building a model workflow.
  • Visualizations for machine learning allow for quick plotting and visual adjustments.

How Does Scikit-learn Work?

Scikit-learn is written primarily in Python and uses NumPy for high-performance linear algebra, as well as for array operations. Some core Scikit-learn algorithms are written in Cython to boost overall performance.

As a higher-level library that includes several implementations of various machine learning algorithms, Scikit-learn lets users build, train, and evaluate a model in a few lines of code.

Scikit-learn provides a uniform set of high-level APIs for building ML pipelines or workflows.

Training and testing.

You use a Scikit-learn ML Pipeline to pass the data through transformers to extract the features and an estimator to produce the model, and then evaluate predictions to measure the accuracy of the model.

  • Transformer: This is an algorithm that transforms or inputs the data for pre-processing.
  • Estimator: This is a machine learning algorithm that trains or fits the data to build a model, which can be used for predictions.
  • Pipeline: A pipeline chains Transformers and Estimators together to specify an ML workflow.

GPU-Accelerated Scikit-learn APIs and End-to-End Data Science

Architecturally, the CPU is composed of just a few cores with lots of cache memory that can handle a few software threads at a time. In contrast, a GPU is composed of hundreds of cores that can handle thousands of threads simultaneously.

The difference between a CPU and GPU.

The NVIDIA RAPIDS suite of open-source software libraries, built on CUDA-X AI, provides the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

RAPIDS’s cuML machine learning algorithms and mathematical primitives follow the familiar Scikit-learn-like API. Popular algorithms like XGBoost, Random Forest, and many others are supported for both single GPU and large data center deployments. For large datasets, these GPU-based implementations can complete 10-50X faster than their CPU equivalents.

Data preparation, model training, and visualization.

With the RAPIDS GPU DataFrame, data can be loaded onto GPUs using a Pandas-like interface, and then used for various connected machine learning and graph analytics algorithms without ever leaving the GPU. This level of interoperability is made possible through libraries like Apache Arrow and allows acceleration for end-to-end pipelines—from data prep to machine learning to deep learning.

RAPIDS supports device memory sharing between many popular data science libraries. This keeps data on the GPU and avoids costly copying back and forth to host memory.

Popular data science libraries.