পদ্ধতির তুলনা করুন

	আধা-পর্যবেক্ষিত লাইটজিবিএম	লাইটজিবিএম
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017–2019	2017
প্রবর্তক	Ke, G. et al. (LightGBM); semi-supervised extension via community practice and research	Ke, G. et al. (Microsoft)
ধরন	Semi-supervised gradient boosting ensemble	Gradient boosting decision tree ensemble
মৌলিক উৎস	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154.
অপর নাম	SSL-LightGBM, pseudo-label LightGBM, self-training LightGBM, semi-supervised GBDT	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
সম্পর্কিত	4	5
সারসংক্ষেপ	Semi-supervised LightGBM combines LightGBM's highly efficient gradient boosting framework with semi-supervised strategies — most commonly pseudo-labeling or self-training — to exploit large pools of unlabeled data alongside a smaller labeled set, improving predictive performance when obtaining labels is costly or time-consuming.	LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy.