scikit-learn VS Keras

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

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scikit-learn Keras
83 80
58,702 61,204
0.9% 0.4%
9.9 9.9
about 2 hours ago 2 days ago
Python Python
BSD 3-clause "New" or "Revised" License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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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 2024-06-17.
  • Essential Deep Learning Checklist: Best Practices Unveiled
    20 projects | | 17 Jun 2024
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations.
  • 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


Posts with mentions or reviews of Keras. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-06-13.
  • Using Google Magika to build an AI-powered file type detector
    4 projects | | 13 Jun 2024
    The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models.
  • Side Quest #3: maybe the real Deepfakes were the friends we made along the way
    3 projects | | 20 May 2024
    def batcher_from_directory(batch_size:int, dataset_path:str, shuffle=False,seed=None) -> """ Return a tensorflow Dataset object that returns images and spectrograms as required. Partly inspired by Args: batch_size: The batch size. dataset_path: The path to the dataset folder which must contain the image folder and audio folder. shuffle: Whether to shuffle the dataset. Default to False. seed: The seed for the shuffle. Default to None. """ image_dataset_path = os.path.join(dataset_path, "image") # create the foundation datasets og_dataset = original_image_path_gen(image_dataset_path), output_signature=tf.TensorSpec(shape=(), dtype=tf.string)) og_dataset = og_dataset.repeat(None) # repeat indefinitely ref_dataset = ref_image_path_gen(image_dataset_path), output_signature=(tf.TensorSpec(shape=(), dtype=tf.string), tf.TensorSpec(shape=(), dtype=tf.bool))) ref_dataset = ref_dataset.repeat(None) # repeat indefinitely # create the input datasets og_image_dataset = x: tf.py_function(load_image, [x, tf.convert_to_tensor(False, dtype=tf.bool)], tf.float32), masked_image_dataset = x: tf.py_function(load_masked_image, [x], tf.float32), ref_image_dataset = x, y: tf.py_function(load_image, [x, y], tf.float32), audio_spec_dataset = x: tf.py_function(load_audio_data, [x, dataset_path], tf.float64), unsync_spec_dataset = x, _: tf.py_function(load_audio_data, [x, dataset_path], tf.float64), # ensure shape as tensorflow does not accept unknown shapes og_image_dataset = x: tf.ensure_shape(x, IMAGE_SHAPE)) masked_image_dataset = x: tf.ensure_shape(x, MASKED_IMAGE_SHAPE)) ref_image_dataset = x: tf.ensure_shape(x, IMAGE_SHAPE)) audio_spec_dataset = x: tf.ensure_shape(x, AUDIO_SPECTROGRAM_SHAPE)) unsync_spec_dataset = x: tf.ensure_shape(x, AUDIO_SPECTROGRAM_SHAPE)) # multi input using full_dataset =, ref_image_dataset, audio_spec_dataset, unsync_spec_dataset), og_image_dataset) # if shuffle: # full_dataset = full_dataset.shuffle(buffer_size=batch_size * 8, seed=seed) # not sure why buffer size is such # batch full_dataset = full_dataset.batch(batch_size=batch_size) return full_dataset
  • Library for Machine learning and quantum computing
    4 projects | | 27 Apr 2024
  • My Favorite DevTools to Build AI/ML Applications!
    9 projects | | 23 Apr 2024
    As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.
  • Release: Keras 3.3.0
    1 project | | 22 Apr 2024
  • Getting Started with Gemma Models
    4 projects | | 15 Apr 2024
    After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.
  • Keras 3.0
    4 projects | | 28 Nov 2023
    All breaking changes are listed here:

    You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch):

  • Keras 3: A new multi-back end Keras
    1 project | | 27 Nov 2023
  • Can someone explain how keras code gets into the Tensorflow package?
    2 projects | /r/tensorflow | 24 Jul 2023
    I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?
  • How popular are libraries in each technology
    21 projects | | 21 Jun 2023
    Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.

What are some alternatives?

When comparing scikit-learn and Keras 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.

MLP Classifier - A handwritten multilayer perceptron classifer using numpy.

Surprise - A Python scikit for building and analyzing recommender systems

Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

tensorflow - An Open Source Machine Learning Framework for Everyone

xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

gensim - Topic Modelling for Humans

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.


pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to:]

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