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The best part about scikit-learn is that it has the variety of regression, classification and clustering algorithms. The page of scikit-learn allows to see which hyper parameters are to be used for my data and what values should I give. Review collected by and hosted on G2.com.
Nothing as of now, but I guess it could be faster for big datasets. Review collected by and hosted on G2.com.
58 out of 59 Total Reviews for scikit-learn
Overall Review Sentiment for scikit-learn
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Users who wish to connect the algorithms to their platforms will find detailed API documentation on the scikit-learn website. Many contributors, authors, and a large international online community support and update Scikit-learn. It is easy to use. The library is published under the BSD license, so it is available for free with only the most basic legal and licensing restrictions. The scikit-learn package is extremely adaptable and useful, and it can be used for a variety of real-world tasks, such as developing neuroimaging, predicting consumer behavior, etc. Review collected by and hosted on G2.com.
It is not a great choice if one prefers in-depth learning. It provides a simple abstraction that can tempt beginner data scientists to continue without first learning the basics. Review collected by and hosted on G2.com.
I like how dynamic scikit-learn library is. it provides preloaded and ready-to-use functions for all sorts of machine learning and data preprocessing algorithms. Review collected by and hosted on G2.com.
The only downside is the lack of native support for deep learning libraries. Review collected by and hosted on G2.com.
Scikit-learn is built on top of efficient numerical libraries, such as NumPy and SciPy, which provide optimized implementations of mathematical and numerical operations. This ensures that the library can handle large datasets and complex computations efficiently, contributing to its robustness and scalability. Review collected by and hosted on G2.com.
While scikit-learn provides a range of tools for feature selection, extraction, and transformation, it does not offer extensive automated feature engineering capabilities found in some specialized libraries. Users may need to manually engineer or select features based on their domain knowledge or explore other feature engineering libraries or techniques. Review collected by and hosted on G2.com.
The best thing, as per me, is there is documentation available of scikit-learn. So, if I sometimes find it difficult to apply some algorithms, I can check the documentation, which helps me. I like this thing. Scikit-learn also provides many inbuilt datasets so that I can use them for practice purposes. Scikit-learn comes with many machine learning algorithm, which makes easy to me for implementing algorithms. I like that it comes with many data manipulation functions to clean my data according to my requirements. Review collected by and hosted on G2.com.
One thing I don't particularly appreciate is that it doesn't have any Deep Learning algorithms. If I want to develop some production-ready algorithm, then scikit-learn is not so great compared to their competitors. Review collected by and hosted on G2.com.
scikit-learn library is very easy to import and ready to use for the python platform. It also contains some sample datasets for trying machine learning algorithms. Review collected by and hosted on G2.com.
There is as such no point that I dislike in scikit-learn library. Most of the commonly used as well as recent machine learning algorithms are available for use Review collected by and hosted on G2.com.
It is very useful in the beginning for data mining and data analysis. Easy to use. It provides maximum efficiency with minimum effort. Data processing, regression, dimension reduction, classification, cluster analysis are the features I use. It's completely free. Review collected by and hosted on G2.com.
It runs slow on large datasets. It can improve on classification. Review collected by and hosted on G2.com.
I really like it when I solve any Machine learning problem, It has a lot of inbuilt ML models that are tough to implement but here those are easy to use. Review collected by and hosted on G2.com.
I feel that It should have much more good deep Neural network models Review collected by and hosted on G2.com.
The 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. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
