hector

hector

Golang machine learning lib. It's forked form github.com/xlvector/hector, but has been rebuild for clearer CLI commands and algorithms scalability.

Supported Algorithms

1. Logistic Regression
2. Factorized Machine
3. CART, Random Forest, Random Decision Tree, Gradient Boosting Decision Tree
4. Neural Network

Dataset Format

Hector support libsvm-like data format. Following is an sample dataset

1 	1:0.7 3:0.1 9:0.4
0	2:0.3 4:0.9 7:0.5
0	2:0.7 5:0.3
...

How to Run

Install

go get github.com/pantsing/hector
hector --help

Here, supported algorithms include

1. lr : logistic regression with SGD and L2 regularization.
2. ftrl : FTRL-proximal logistic regreesion with L1 regularization. Please review this paper for more details "Ad Click Prediction: a View from the Trenches".
3. ep : bayesian logistic regression with expectation propagation. Please review this paper for more details "Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine"
4. fm : factorization machine
5. cart : classifiaction tree
6. cart-regression : regression tree
7. rf : random forest
8. rdt : random decision trees
9. gbdt : gradient boosting decisio tree
10. linear-svm : linear svm with L1 regularization
11. svm : svm optimizaed by SMO (current, its linear svm)
12. l1vm : vector machine with L1 regularization by RBF kernel
13. knn : k-nearest neighbor classification

Benchmark

Binary Classification

Following are datasets used in benchmarks, You can find them from UCI Machine Learning Repository

1. heart
2. fourclass

I will do 5-fold cross validation on the dataset, and use AUC as evaluation metric. Following are the results: