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Machine learning technology in biodiesel research: A review
Biodiesel has the potential to significantly contribute to making transportation fuels more
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
A random forest guided tour
The random forest algorithm, proposed by L. Breiman in 2001, has been extremely
successful as a general-purpose classification and regression method. The approach, which …
successful as a general-purpose classification and regression method. The approach, which …
Generalized random forests
S Athey, J Tibshirani, S Wager - 2019 - projecteuclid.org
Generalized random forests Page 1 The Annals of Statistics 2019, Vol. 47, No. 2, 1148–1178
https://doi.org/10.1214/18-AOS1709 © Institute of Mathematical Statistics, 2019 GENERALIZED …
https://doi.org/10.1214/18-AOS1709 © Institute of Mathematical Statistics, 2019 GENERALIZED …
Estimation and inference of heterogeneous treatment effects using random forests
Many scientific and engineering challenges—ranging from personalized medicine to
customized marketing recommendations—require an understanding of treatment effect …
customized marketing recommendations—require an understanding of treatment effect …
Machine unlearning for random forests
Responding to user data deletion requests, removing noisy examples, or deleting corrupted
training data are just a few reasons for wanting to delete instances from a machine learning …
training data are just a few reasons for wanting to delete instances from a machine learning …
Application of machine learning in disease prediction
PS Kohli, S Arora - 2018 4th International conference on …, 2018 - ieeexplore.ieee.org
The application of machine learning in the field of medical diagnosis is increasing gradually.
This can be contributed primarily to the improvement in the classification and recognition …
This can be contributed primarily to the improvement in the classification and recognition …
Predicting compressive strength of geopolymer concrete using machine learning
The anaconda software required python code in order to run the utilized individual K-nearest
neighbor (KNN), random forest regression (RFR), and linear regression (LR) models. The …
neighbor (KNN), random forest regression (RFR), and linear regression (LR) models. The …
A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow …
Objective Some researchers have studied about early prediction and diagnosis of major
adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this …
adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this …