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Scikit-learn in Python: Features, Prerequisites, Pros & Cons

You must realize how important it is to have a robust library if you are a regular at Python programming. When it comes to free Machine Learning libraries for Python, scikit-learn is the best you can get! sklearn or scikit-learn in Python is a free library that simplifies the task of coding and applying Machine Learning algorithms in Python.

Besides supporting Python scientific and numerical libraries like SciPy and NumPy, scikit-learn features a host of different algorithms like random forests, support vector machines, and k-neighbors. So, let’s get to know some of the fundamental aspects of one of the essential Machine Learning tools you can find.

What is sklearn or scikit-learn in Python?

Sklearn or scikit-learn in Python is by far one of the most useful open-source libraries available that you can use for Machine Learning in Python. The scikit-learn library is an exhaustive collection of the most efficient tools for statistical modeling and Machine Learning. Some of these tools include regression, classification, dimensionality reduction, and clustering. 

The scikit-learn library is primarily written in Python and built upon SciPy, NumPy, and Matplotlib. The library uses a unified and consistent Python interface to implement various pre-processing, Machine Learning, visualization, and cross-validation algorithms. 

A brief history of Scikit-learn 

Known initially as scikit-learn, sklearn in Python was developed by David Cournapeau in 2007 as part of Google’s summer of code project. Subsequently, Gael Varoquaux, Fabian Pedregosa, Alexandre Gramfort, and Vincent Michel, from the French Institute for Research in Computer Science and Automation, publicly released a v0.1 beta version in the year 2010.

Since then, newer versions of scikit-learn have been released, with the latest version 0.23.1 released in May 2020. Scikit-learn is a community-driven project where anyone can contribute towards its development. Microsoft, Intel, and NVIDIA are among the project’s top sponsors.

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Essential features of scikit-learn 

The Machine Learning library scikit-learn in Python comes with a load of features to simplify Machine Learning. Here we will discuss some of them:

  • Supervised learning algorithms: Any supervised Machine Learning algorithm that you may have heard of has a very high possibility of belonging to the scikit-learn library. The scikit-learn toolkit has a repertoire of such supervised learning algorithms, which includes – Generalized linear models such as Linear regression, Decision Trees, Support Vector Machines, and Bayesian methods. 
  • Unsupervised learning algorithms: This algorithm collection includes factoring, cluster analysis, principal component analysis, and unsupervised neural networks.
  • Feature extraction: Using scikit-learn, you can extract features from text and images.
  • Cross-validation: The accuracy and validity of supervised models on unseen data can be checked with the help of scikit-learn.
  • Dimensionality Reduction: With this feature, the number of attributes in data can be reduced for subsequent visualization, summarization, and feature selection.
  • Clustering: This feature allows the grouping of unlabeled data.
  • Ensemble methods: The predictions of several supervised models can be combined by using this feature.

Read more: 6 Types of Supervised Learning You Must Know About

Prerequisites to starting scikit-learn

Before you begin using the latest release of scikit-learn, make sure you have installed the following libraries:

  • Python (>=3.5)
  • NumPy (>= 1.11.0)
  • SciPy (>= 0.17.0)li
  • Joblib (>= 0.11)
  • Matplotlib (>= 1.5.1): this library is required for scikit-learn plotting capabilities.
  • Pandas (>= 0.18.0): this is required for data structure and analysis.

Installing scikit-learn

You can follow either one of the following two methods for scikit-learn installation:

  • Using pip

 – Scikit-learn can be installed via pip and the command line for the same is as follows:

pip install -U scikit-learn

  • Using conda

 – Scikit-learn can also be installed via conda and the command line used as follows:

conda install scikit-learn

If you do not have NumPy and SciPy installed, you can install them via pip or conda. Anaconda and Canopy are two other Python distributions that can be used to learn the latest scikit-learn version.

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Pros and cons of scikit-learn

Pros:

  • The library is distributed under the BSD license, making it free with minimum legal and licensing restrictions.
  • It is easy to use.
  • The scikit-learn library is very versatile and handy and serves real-world purposes like the prediction of consumer behavior, the creation of neuroimages, etc.
  • Scikit-learn is backed and updated by numerous authors, contributors, and a vast international online community.
  • The scikit-learn website provides elaborate API documentation for users who want to integrate the algorithms with their platforms.

Con:

  • It is not the best choice for in-depth learning.

Learn more: How does Unsupervised Machine Learning Work?

Conclusion

The growth and popularity of Machine Learning language call for efficient tools, and sklearn in Python serves the need for beginners as well as those solving supervised learning problems. Efficiency and versatility of use make scikit-learn one of the prime choices of academic and industrial organizations for performing various operations.

What is scikit-learn in Python?

Scikit-learn is a free software library for the Python programming language that provides a collection of algorithms for machine learning and data mining. It features various classification, regression and clustering algorithms including support vector machines, random forests, boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. It is licensed under the BSD license.

What are the limitations of scikit learn in Python?

Scikit-learn is a fantastic tool for exploring, transforming and classifying data. But it is optimized for learning algorithms, such as Support Vector Machines (SVMs), logistic regression, and Linear Discriminant Analysis (LDA). It is not optimized for graph algorithms, and it is not very good at string processing. For example, scikit-learn does not provide a built-in way to produce a simple word cloud. Scikit-learn doesn’t have a strong linear algebra library, hence scipy and numpy are used. It doesn’t contain a plotting library, but it allows to use different plotting libraries.

Can Scikit be used for deep learning?

Scikit is just a collection of a few libraries. So, any library can be used in it. Deep learning is very popular in the market. Keras and Theano are the most popular deep learning frameworks for Python. They are great for research and provide the best performance. But for production, we have to use tools like TensorFlow, Caffe and DeepLearning4J. Scikit-learn provides several tools like RandomForest, GradientBoosting, NeuralNet, etc. which are really helpful for beginners. These are easier to write and are good enough for most of the use-cases.

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