MLlib vs. scikit-learn
A side-by-side comparison based on G2 review data.
| Metric | MLlib | scikit-learn |
|---|---|---|
| Overall rating | 4.1 | 4.8 |
| Review count | 14 | 60 |
| Ease of use | 9.0 | 9.6 |
| Ease of setup | 8.9 | 9.7 |
| Quality of support | 7.7 | 9.4 |
| Meets requirements | 8.7 | 9.6 |
| NPS | 35.714286 | 90 |
| Small Business | 21.4% | 28.3% |
| Mid-Market | 50.0% | 31.7% |
| Enterprise | 28.6% | 40.0% |
| Total page views | 22 | 197 |
| Total LLM citations | 6 | 381 |
MLlib
MLlib is Spark's machine learning (ML) library that make practical machine learning scalable and easy it provides ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering, feature extraction, transformation, dimensionality reduction, and selection, tools for constructing, evaluating, and tuning ML Pipelines, saving and load algorithms, models, and Pipelines and linear algebra, statistics, data handling, etc.
scikit-learn
Scikit-learn is a software machine learning library for the Python programming language that has a various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
- Usage Frequency
- Machine Learning
- Ease of Use