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What is Scikit-learn

Scikit-learn is a well-documented and easy-to-use library that facilitates the application of machine learning algorithms by using simple methods, which ultimately enables beginners to model data without the need for deep knowledge of the math behind the algorithms. Additionally, thanks to the ease of use of this library, it allows the user to implement different approximations (create different models) for a data problem. Moreover, by removing the task of coding the algorithm, scikit-learn allows teams to focus their attention on analyzing the results of the model to arrive at crucial conclusions.

Spotify, a world leading company in the field of music streaming, uses scikit-learn, since it allows them to implement multiple models for a data problem, which are then easily connectable to their existing development. This process improves the process of arriving at a useful model, while allowing the company to plug them into their current app with little effort.

On the other hand, uses scikit-learn due to the wide variety of algorithms that the library offers, which allows them to fulfill the different data analysis tasks that the company relies on, such as building recommendation engines, detecting fraudulent activities, and managing the customer service team.

Considering the preceding points, this chapter begins with an explanation of scikitlearn and its main uses and advantages, and then moves on to provide a brief explanation of the scikit-learn API syntax and features. Additionally, the process to represent, visualize, and normalize data is shown. The aforementioned information will be useful to understand the different steps taken to develop a machine learning model.


Created in 2007 by David Cournapeau as part of a Google Summer of Code project, scikit-learn is an open source Python library made to facilitate the process of building models based on built-in machine learning and statistical algorithms, without the need for hard-coding. The main reasons for its popular use are its complete documentation, its easy-to-use API, and the many collaborators who work every day to improve the library.

Note: You can find the documentation for scikit-learn at

Scikit-learn is mainly used to model data, and not as much to manipulate or summarize data. It offers its users an easy-to-use, uniform API to apply different models, with little learning effort, and no real knowledge of the math behind it, required.

Note: Some of the math topics that you need to know about to understand the models are linear algebra, probability theory, and multivariate calculus. For more information on these models, visit:

The models available under the scikit-learn library fall into two categories: supervised and unsupervised, both of which will be explained in depth in later sections. This category classification will help to determine which model to use for a particular dataset to get the most information out of it.

Besides its main use for interpreting data to train models, scikit-learn is also used to do the following:

  • Perform predictions, where new data is fed to the model to predict an outcome
  • Carry out cross validation and performance metrics analysis to understand the results obtained from the model, and thereby improve its performance
  • Obtain sample datasets to test algorithms over them
  • Perform feature extraction to extract features from images or text data

Although scikit-learn is considered the preferred Python library for beginners in the world of machine learning, there are several large companies around the world using it, as it allows them to improve their product or services by applying the models to already existing developments. It also permits them to quickly implement tests over new ideas.

Note: You can visit the following website to find out which companies are using scikitlearn and what are they using it for:

In conclusion, scikit-learn is an open source Python library that uses an API to apply most machine learning tasks (both supervised or unsupervised) to data problems. Its main use is for modeling data; nevertheless, it should not be limited to that, as the library also allows users to predict outcomes based on the model being trained, as well as to analyze the performance of the model.

Advantages of Scikit-Learn

The following is a list of the main advantages of using scikit-learn for machine learning purposes:

  • Ease of use: Scikit-learn is characterized by a clean API, with a small learning curve in comparison to other libraries such as TensorFlow or Keras. The API is popular for its uniformity and straightforward approach. Users of scikit-learn do not necessarily need to understand the math behind the models.
  • Uniformity: Its uniform API makes it very easy to switch from model to model, as the basic syntax required for one model is the same for others.
  • Documentation/Tutorials: The library is completely backed up by documentation, which is effortlessly accessible and easy to understand. Additionally, it also offers step-by-step tutorials that cover all of the topics required to develop any machine learning project.
  • Reliability and Collaborations: As an open source library, scikit-learn benefits from the inputs of multiple collaborators who work each day to improve its performance. This participation from many experts from different contexts helps to develop not only a more complete library but also a more reliable one.
  • Coverage: As you scan the list of components that the library has, you will discover that it covers most machine learning tasks, ranging from supervised models such as classification and regression algorithms to unsupervised models such as clustering and dimensionality reduction. Moreover, due to its many collaborators, new models tend to be added in relatively short amounts of time.

Disadvantages of Scikit-Learn

The following is a list of the main disadvantages of using scikit-learn for machine learning purposes:

  • Inflexibility: Due to its ease of use, the library tends to be inflexible. This means that users do not have much liberty in parameter tuning or model architecture. This becomes an issue as beginners move to more complex projects.
  • Not Good for Deep Learning: As mentioned previously, the performance of the library falls short when tackling complex machine learning projects. This is especially true for deep learning, as scikit-learn does not support deep neural networks with the necessary architecture or power.

In general terms, scikit-learn is an excellent beginner's library as it requires little effort to learn its use and has many complementary materials thought to facilitate its application. Due to the contributions of several collaborators, the library stays up to date and is applicable to most current data problems.

On the other hand, it is a fairly simple library, not fit for more complex data problems such as deep learning. Likewise, it is not recommended for users who wish to take its abilities to a higher level by playing with the different parameters that are available in each model.

Hope you enjoyed reading the article. If you want to learn more about Scikit and other machine learning libraries, you should explore the course, Machine Learning Fundamentals. Following a hands-on approach to explain the underlying concepts of ML, Machine Learning Fundamentals is packed with numerous projects that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context

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Tomas Alabes

Software Engineer, author, blogger and obsessive learner, from Argentina living in Silicon Valley



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