neptune-contrib VS scikit-learn

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

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neptune-contrib scikit-learn
0 44
26 51,531
- 0.7%
1.0 9.9
12 months ago 5 days ago
Python Python
MIT License BSD 3-clause "New" or "Revised" License
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 neptune-contrib. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning neptune-contrib yet.
Tracking mentions began in Dec 2020.


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 2022-10-05.

What are some alternatives?

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

Keras - Deep Learning for humans

Surprise - A Python scikit for building and analyzing recommender systems

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

tensorflow - An Open Source Machine Learning Framework for Everyone

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.


seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)

MLflow - Open source platform for the machine learning lifecycle

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

TFLearn - Deep learning library featuring a higher-level API for TensorFlow.

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration