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.
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You can find the documentation for scikit-learn at the following link: http://scikit-learn.org.
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.
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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: https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568.
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.
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You can visit the following website to find out which companies are using scikit-learn and what are they using it for: http://scikit-learn.org/stable/testimonials/testimonials.html.
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.
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.
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.