What is scikit-learn?

Machine learning is one of the most important disciplines for any budding data scientist (That’s you!). So uncovering tools that make this technology easier is always a good thing, right? Scikit-learn lets you define complex machine learning algorithms and compare them, making it a great tool to add to your tech stack. 

Discover the answer to the question, “What is scikit-learn?” and find out how The Data Incubator can expand your knowledge of data science below.

Scikit-learn, Explained

So, what is scikit-learn? Well, it’s an open-source machine learning library for Python, one of the most popular programming languages. Built on top of libraries such as pandas and NumPy, scikit-learn provides various tools that support data scientists in performing machine learning tasks, such as data analysis, data mining, supervised and unsupervised learning, feature extraction, preprocessing and evaluating machine learning models.

Did you know that data scientist David Cournapeau created scikit-learn as a Google Summer of Code project in 2007? Its first public release appeared in February 2010, and new iterations of the library have followed every three months. 

Scikit-learn has a community of users who continue to improve the tool by adding new algorithms and features. These users have also created tutorials and documentation that help you understand how to use scikit-learn and incorporate it into your data science workflows.

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Benefits

Here are some of the advantages of using scikit-learn in data science:

Consistent Design

Scikit-learn has a consistent design that integrates well into the Python ecosystem. That makes it easy to understand and use when performing machine learning tasks in your future data science career. 

Switch Between Algorithms

Because scikit-learn has a common set of interfaces, you can easily switch between different algorithms and compare their performance. That will allow you to make more insightful predictions when analyzing data sets.

Prepare Data for Machine Learning

Scikit-learn’s preprocessing and feature extraction capabilities prepare data for machine learning. You can normalize, scale, and encode data variables and complete tasks quickly. 

Model Evaluation and Selection

Scikitset provides tools for model evaluation and selection, letting you complete complicated tasks such as cross-validation, random search and grid search.

Supervised Machine Learning

Sci-kit provides tools for supervised machine learning, which uses labeled datasets to train algorithms that classify data sets and predict outcomes. That can help you make better decisions when analyzing data.

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Scikit-learn Cons

Here are some disadvantages of using scikit-learn:

Doesn’t Scale Well

Scikit-learn suits small and medium-sized data sets and doesn’t scale well if you want to analyze information from larger data sets. Another machine learning tool will be better for bigger data projects. 

Only Uses Python

Scikit-learn utilizes Python, so you’ll need to master this language before using the tool. 

Doesn’t Support Advanced Machine Learning or Deep Learning

Scikit-learn doesn’t support more advanced machine learning techniques or deep learning. 

Final Word

Scikit-learn is a machine learning library that you might use in your future data science career. It makes your work easier by providing tools for machine learning tasks, such as data analysis, preprocessing and feature extraction. Enrolling in a data science program will provide you with more answers to the question, “What is scikit-learn?”