| pommedeterresautee/fastrtext |
97 |
|
1 |
1 |
over 6 years ago |
11 |
October 27, 2019 |
8 |
other |
C++ |
| R wrapper for fastText |
| zmjones/edarf |
61 |
|
0 |
1 |
over 8 years ago |
3 |
March 06, 2017 |
2 |
mit |
R |
| exploratory data analysis using random forests |
| davpinto/fastknn |
55 |
|
0 |
0 |
over 8 years ago |
0 |
|
1 |
|
R |
| Fast k-Nearest Neighbors Classifier for Large Datasets |
| spsanderson/tidyAML |
53 |
|
0 |
0 |
over 2 years ago |
1 |
February 16, 2023 |
2 |
other |
R |
| Auto ML for the tidyverse |
| liquidSVM/liquidSVM |
45 |
|
0 |
3 |
over 6 years ago |
8 |
September 14, 2019 |
11 |
agpl-3.0 |
C++ |
| Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression. |
| mkearney/tweetbotornot2 |
37 |
|
0 |
0 |
over 6 years ago |
0 |
|
0 |
other |
R |
| 🔍🐦🤖 Detect Twitter Bots! |
| raviolli77/machineLearning_breastCancer_Python |
26 |
|
0 |
0 |
about 6 years ago |
0 |
|
3 |
mit |
HTML |
| Machine Learning Applications using Sklearn, matplotlib, pandas, and seaborn |
| ropensci/coder |
18 |
|
0 |
0 |
over 3 years ago |
0 |
|
4 |
mit |
TeX |
| Classification of Cases into Deterministic Categories |
| sarahromanes/multiDA |
11 |
|
0 |
0 |
almost 7 years ago |
0 |
|
1 |
|
R |
| High Dimensional Discriminant Analysis in R :sparkles: |
| bearloga/MLPUGS |
9 |
|
0 |
0 |
over 6 years ago |
1 |
July 06, 2016 |
3 |
other |
R |
| An R package for Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains) |
