scikit-learn
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What is scikit-learn?
Scikit-learn is a free software machine learning library for Python. It features various classification, regression and clustering algorithms, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. It is commonly used for tasks like predictive data analysis, building and evaluating machine learning models, and performing statistical analysis on datasets.
What other technologies are related to scikit-learn?
scikit-learn Competitor Technologies
TensorFlow
TensorFlow is a machine learning framework that offers similar capabilities to scikit-learn for building and training models, but with a focus on deep learning and neural networks.
PyTorch
PyTorch is another machine learning framework, like TensorFlow, that competes with scikit-learn, particularly in the domain of deep learning and neural networks.
Keras
Keras is a high-level API for building and training neural networks. It can be used as a competitor since Scikit-learn also has some neural network capabilities, though Keras focuses almost exclusively on neural nets. Can also be complementary by using Keras models with scikit-learn wrappers.
XGBoost
XGBoost is a gradient boosting framework that offers similar functionality to scikit-learn's gradient boosting methods and can be considered a competitor due to its widespread adoption and performance.
SparkML
SparkML is Apache Spark's machine learning library, offering scalable machine learning algorithms, making it a competitor for large datasets where scikit-learn might be limited.
statsmodels
statsmodels is a Python library that provides classes and functions for estimating and testing statistical models. Although complementary at times, it offers statistical modeling capabilities which are similar to those found in scikit-learn, thereby also making it a competitor.
MXNet
MXNet is a deep learning framework that offers similar capabilities to scikit-learn for building and training models, particularly neural networks, making it a competitor.
LightGBM
LightGBM is a gradient boosting framework, similar to XGBoost, and competes with scikit-learn's gradient boosting methods, known for its speed and efficiency.
Number of organizations that mention technology
20,132
scikit-learn
30,502
TensorFlow
29,262
PyTorch
10,426
Keras
2,669
XGBoost
1,029
SparkML
671
statsmodels
842
MXNet
683
LightGBM
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scikit-learn Complementary Technologies
Pandas
Pandas is a library for data manipulation and analysis, providing data structures like DataFrames that are often used as input to scikit-learn models.
NumPy
NumPy is a fundamental library for numerical computing in Python, providing arrays and mathematical functions that scikit-learn relies on.
SciPy
SciPy builds on NumPy and provides additional scientific computing tools and algorithms that can be used in conjunction with scikit-learn.
Number of organizations that mention technology
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Which job functions commonly mention scikit-learn?
Machine Learning
16.7% of all Machine Learning jobs mention scikit-learn
View 17.6k
jobs on Sumble
Applied Scientist
16.1% of all Applied Scientist jobs mention scikit-learn
View 1.8k
jobs on Sumble
Data Scientist
13.2% of all Data Scientist jobs mention scikit-learn
View 32k
jobs on Sumble
AI Engineer
9.9% of all AI Engineer jobs mention scikit-learn
View 8.5k
jobs on Sumble
Data, Analytics & Machine Learning
3.5% of all Data, Analytics & Machine Learning jobs mention scikit-learn
View 62.3k
jobs on Sumble
Which organizations are mentioning scikit-learn?
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