scikit-learn VS gensim

Compare scikit-learn vs gensim and see what are their differences.

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scikit-learn gensim
82 18
58,344 15,311
0.9% 1.2%
9.9 7.0
6 days ago 6 days ago
Python Python
BSD 3-clause "New" or "Revised" License GNU Lesser General Public License v3.0 only
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of scikit-learn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-04-09.
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    1 project | | 5 May 2024
    Online Courses: Coursera: "Machine Learning" by Andrew Ng edX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: Kaggle Learn: Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By understanding the core concepts of logistic regression, its limitations, and exploring further resources, you'll be well-equipped to navigate the exciting world of machine learning!
  • AutoCodeRover resolves 22% of real-world GitHub in SWE-bench lite
    8 projects | | 9 Apr 2024
    Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:

    - From sympy: AutoCodeRover's patch for it:

    - Another one from scikit-learn: AutoCodeRover's patch ( modified a few lines below (compared to the developer patch) and wrote a different comment.

    There are more examples in the results directory (

  • Polars
    11 projects | | 8 Jan 2024
    sklearn is adding support through the dataframe interchange protocol ( scipy, as far as I know, doesn't explicitly support dataframes (it just happens to work when you wrap a Series in `np.array` or `np.asarray`). I don't know about PyTorch but in general you can convert to numpy.
  • [D] Major bug in Scikit-Learn's implementation of F-1 score
    2 projects | /r/MachineLearning | 8 Dec 2023
    Wow, from the upvotes on this comment, it really seems like a lot of people think that this is the correct behavior! I have to say I disagree, but if that's what you think, don't just sit there upvoting comments on Reddit; instead go to this PR and tell the Scikit-Learn maintainers not to "fix" this "bug", which they are currently planning to do!
  • Contraction Clustering (RASTER): A fast clustering algorithm
    1 project | | 27 Nov 2023
  • Ask HN: Learning new coding patterns – how to start?
    3 projects | | 10 Nov 2023
    I was in a similar boat to yours - Worked in data science and since then have made a move to data engineering and software engineering for ML services.

    I would recommend you look into the Design Patterns book by the Gang of Four. I found it particularly helpful to make extensible code that doesn't break specially with abstract classes, builders and factories. I would also recommend looking into the book The Object Oriented Thought Process to understand why traditional OOP is build the way it is.

    You can also look into the source code of popular data science libraries such as sklearn ( and see how a lot of them have Base classes to define shared functionality between object of the same nature.

    As others mentioned, I would also encourage you to try and implement design patterns in your everyday work - maybe you can make a Factory to load models or preprocessors that follow the same Abstract class?

  • Transformers as Support Vector Machines
    1 project | | 3 Sep 2023
    It looks like you've been the victim of some misinformation. As Dr_Birdbrain said, an SVM is a convex problem with unique global optimum. sklearn.SVC relies on libsvm which initializes the weights to 0 [0]. The random state is only used to shuffle the data to make probability estimates with Platt scaling [1]. Of the random_state parameter, the sklearn documentation for SVC [2] says

    Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.




  • How to Build and Deploy a Machine Learning model using Docker
    5 projects | | 30 Jul 2023
    Scikit-learn Documentation
  • Planning to get a laptop for ML/DL, is this good enough at the price point or are there better options at/below this price point?
    1 project | /r/developersIndia | 17 Jun 2023
  • Link Prediction With node2vec in Physics Collaboration Network
    4 projects | | 16 Jun 2023
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy.


Posts with mentions or reviews of gensim. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-23.

What are some alternatives?

When comparing scikit-learn and gensim you can also consider the following projects:

Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.

Surprise - A Python scikit for building and analyzing recommender systems

MLflow - Open source platform for the machine learning lifecycle

Keras - Deep Learning for humans

tensorflow - An Open Source Machine Learning Framework for Everyone

H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)


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