Scikit-learn is a software machine learning library for the Python programming language that has a various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
Customer Reviews
Devwrat T.
Advanced user of scikit-learnThe best aspect about this framework is the availability of well integrated algorithms within the Python development environment. It is quite easy to install within most Python IDEs and relatively easy to use as well. A lot of tutorials are accessible online which supplements understanding this library allowing to become proficient in machine learning. It was clearly built with a software engineering mindset and nevertheless, it is very flexible for research ventures. Being built on top of multiple math-based and data libraries, scikit-learn allows seamless integration between them all. Being able to use numpy arrays and pandas dataframes within the scikit-learn environment removes the need for additional data transformation. That being said, one should definitely get familiar with this easy to use library if they plan on becoming a data-driven professional. You could build a simple machine learning model with just 10 lines of code! With tons of features like model validation, data splitting for training/testing and various others, scikit-learn's open source approach facilitates a manageable learning curve.
One issue that has persisted and troubled me since quite some time is the lack of categorical variables transformation capabilities (it is much easier in libraries like tensorflow). It is comparatively slower than tensorflow when it comes to big datasets and this is something that should be adopted soon especially in the era of big data technologies. However, with the frequency of updates, I believe most issues get resolved really quickly making it a robust package for machine learning development.
Highly encourage those breaking into the field of Data Science/Analytics to dive deep into this library considering the amount of resources available online. With the easy to use interface, being open-source and flexibility and adaptability with other frameworks, machine learning could not get any easier! I personally feel starting off with scikit-learn will help you adapt to other big data tools surrounding machine learning like PySpark.
Since I am a data science professional, I use scikit-learn to create predictive analytics models for demand-forecasting and other applications. Scikit-learn is the best framework out there to assist with machine learning model development that has allowed me to participate and win in a many online competitions. One of the primary benefits is the ease of learning and the ease of use of this library. Coupled with the amount of resources available online for this library, it is the best ML library out there.
