multi-task-gradient-boosting

Usage

Refer run experiment code. If you want to split csv file into n pairs(training data and test data).

Run Experiment Code

• MALSAR_experiment

  • rMTFL: run example_rMTFL.m
  • Lasso: run example_Lasso.m
  • Trace: run example_Trace.m
  • Dirty: run example_Dirty.m

• Multi-Task GBDT

  • run main.cpp

Parameter Guide for Multi-Task GBDT

• log_path: The file path for storing log
• path: The path of dataset
• eval_metric: evaluation for performance
• dataset_name: dataset name
• feature_size: The number of feature
• task_num: The number of task
• max_num_round: The iterations of two-staged model
• common_num_round: The max iterations of common training stage
• regularization: The regularization of gain score (options: entropy, variance)
• beta: The coefficient of variance (The value is zero for entropy-based method)
• early_stopping_round: like xgboost (0 indicate does not use it, default value is 10)

Experiment Results

Results from the real datasets for RMSE over 10 repetitions. The statistically best model is highlighted in bold.

(Independent-XGBoost: Train T models for T tasks, Aggregate-XGBoost: Train one model for T tasks(regard T tasks as one task))

Dataset	Measure	Trace	LASSO	rMTFL	Dirty	Independent-XGBoost	Aggregate-XGBoost	Variance-based Multi-Task GBDT	Entropy-based Multi-Task GBDT
school	RMSE	11.452 +/- 0.012	11.216 +/- 0.010	10.467 +/- 0.027	10.411 +/- 0.029	10.767 +/- 0.007	11.099 +/- 0.012	8.993 +/- 0.232	8.998 +/- 0.239

cite

@inproceedings{DBLP:conf/cikm/ZhangL19,
  author    = {Ya{-}Lin Zhang and
               Longfei Li},
  title     = {Interpretable {MTL} from Heterogeneous Domains using Boosted Tree},
  booktitle = {Proceedings of the 28th {ACM} International Conference on Information
               and Knowledge Management, {CIKM} 2019, Beijing, China, November 3-7,
               2019},
  pages     = {2053--2056},
  year      = {2019},
  crossref  = {DBLP:conf/cikm/2019},
  url       = {https://doi.org/10.1145/3357384.3358072},
  doi       = {10.1145/3357384.3358072},
  timestamp = {Mon, 04 Nov 2019 11:09:32 +0100},
  biburl    = {https://dblp.org/rec/bib/conf/cikm/ZhangL19},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}