Võrdle meetodeid

	LSTM	Juhuslik mets	Korduv närvivõrk	XGBoost
Valdkond	Süvaõpe	Masinõpe	Süvaõpe	Masinõpe
Perekond	Machine learning	Machine learning	Machine learning	Machine learning
Tekkeaasta	1997	2001	1986–1990	2016
Looja	Hochreiter, S. & Schmidhuber, J.	Breiman, L.	Rumelhart, D. E.; Elman, J. L.	Chen, T. & Guestrin, C.
Tüüp	Recurrent neural network (gated memory cell)	Ensemble (bagging of decision trees)	Sequential neural network	Ensemble (gradient-boosted decision trees)
Algallikas	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Rööpnimetused	LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	RNN, Elman network, Jordan network, simple recurrent network	XGBoost, extreme gradient boosting, scalable tree boosting
Seotud	5	4	3	5
Kokkuvõte	LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.