The sklearn documentation is extremely good, and a large number of machine learning analyses can be done using this library. Review collected by and hosted on G2.com.
The number of different hyperparameters to set is huge. Review collected by and hosted on G2.com.
The best thing about scikit learn is that it makes implementing and using machine learning algorithms a much easy play.I have been using scikit learn since the start of my career and even during my graduate days I used scikit learn.It has been improving since then and also updating the algorithms.Using scikit learn will really pace up your tasks of implementing ML tasks for your service. Review collected by and hosted on G2.com.
Nothing to dislike about scikit learn.I would say it is really a good library. Review collected by and hosted on G2.com.
It is a solution for Machine Learning tasks. You have optimisation techniques also(use gridsearchcv or randomsearchcv). It also has a cheatsheet or path to describe which algorithm a user should use. Review collected by and hosted on G2.com.
There is no implementation of Catbooster classifier, lightGBM classifier and many more. Review collected by and hosted on G2.com.
The best thing that I started liking about scikit learn is the ease of creating and running a machine learning algorithm for any model.If you need a KNN for your face recognition just call the Knn classifier with the proper hyper parameters and use it in your face recognition model with very less lines of code and much simplicity.If you need to use a linear regression model then just call its object,have your own data trained on it and predict when required.Its very simple to use this and that's what makes it most interesting.Apart from this it comes with many custom datasets which can be directly imported and used. Review collected by and hosted on G2.com.
Nah,nothing found yet to dislike about this awesome library. Review collected by and hosted on G2.com.
Scikit-learn is extremely scalable and great for beginners especially. My main experience has been using their support vector classifier, which is ideal for our project in mapping ultrasound imagery to movements of the hand. Review collected by and hosted on G2.com.
Documentation could be a bit better, but other than that it's incredibly reliable and consistent. Review collected by and hosted on G2.com.
Scikit-learn can be used for almost all the machine learning tasks as it consists of tools for most of the standard machine learning tasks like classification, clustering, regression and dimensionality reduction. Review collected by and hosted on G2.com.
R is more focused on statistics than scikit-learn. For example R provides more details regarding regression than scikit-learn Review collected by and hosted on G2.com.
I love the fact that almost every machine learning algorithm are made easy in the framework it's very easy to use. It has so many functionalities Review collected by and hosted on G2.com.
It doesn't have any deep learning version it's mainly for machine learning I e it's not robust Review collected by and hosted on G2.com.
scikit-learn provides a clean and consistent interface to tons of different models Review collected by and hosted on G2.com.
Scikit learn can be hard to learn if you don't have previous experience with python Review collected by and hosted on G2.com.
What not to like, gives you the power to train machine learning models abstracting out how it is working underneath. It can be scary sometimes to know how ML algorithms work in theory and it gets scarier when you got to put into functioning code but with scikit learning you dont have to worry about the underlying implementation and just get started with Machine Learning Review collected by and hosted on G2.com.
Could not find anything to dislike till now 😊 Review collected by and hosted on G2.com.
scikit learn basically is the library for python that includes all the machine learning algorithms in it which are perfectly coded to make your work easy.It helps us to look at the application part rather than the implementation part and also reduces our time by eliminating the need of coding the algorithm from scratch.It is a famous and widely used library and also is supported by many open source developers which makes its algorithm very better than any else.Also it has a large variety of dataset which can also be used for testing like iris dataset so it helps a lot during development and testing the code. Review collected by and hosted on G2.com.
I actually love this library and spent almost all my worktime using this and have nothing to dislike about it. Review collected by and hosted on G2.com.