Results 111 to 120 of about 5,516,106 (266)
Analyzing Household Expenditures with Generalized Random Forests
Cauchy: Jurnal Matematika Murni dan AplikasiThis study investigates the performance of Generalized Random Forest (GRF), which has been known to be useful in understanding heterogeneous treatment effects (HTE) and non-linear relationships in high-dimensional data.
Eriski Isnanda +2 more
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Random Forests for Global and Regional Crop Yield Predictions
PLoS ONE, 2016 Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to ...
Jig Han Jeong +10 more
semanticscholar +1 more source
2008 Seventh International Conference on Machine Learning and Applications, 2008 When using the output of classifiers to calculate the expected utility of different alternatives in decision situations, the correctness of predicted class probabilities may be of crucial importance. However, even very accurate classifiers may output class probabilities of rather poor quality.
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Gene selection and classification of microarray data using random forest [PDF]
, 2006 Ramón Díaz‐Uriarte +1 more
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Geocarto International, 2019 Machine learning algorithms such as Random Forest (RF) are being increasingly applied on traditionally geographical topics such as population estimation.
S. Georganos +8 more
semanticscholar +1 more source
On the asymptotics of random forests
Journal of Multivariate Analysis, 2016 The last decade has witnessed a growing interest in random forest models which are recognized to exhibit good practical performance, especially in high-dimensional settings. On the theoretical side, however, their predictive power remains largely unexplained, thereby creating a gap between theory and practice. The aim of this paper is twofold. Firstly,
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BMC Research Notes, 2017 Background Uganda just like any other Sub-Saharan African country, has a high under-five child mortality rate. To inform policy on intervention strategies, sound statistical methods are required to critically identify factors strongly associated with ...
Justine B. Nasejje, Henry Mwambi
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Finding minimum spanning forests in logarithmic time and linear work using random sampling [PDF]
, 1996 Richard Cole +2 more
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Sparse Projection Oblique Randomer Forests
, 2019 Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings.
Browne, James +10 more
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PARALLEL ALGORITHMS OF RANDOM FORESTS FOR CLASSIFYING VERY LARGE DATASETS
Tạp chí Khoa học Đại học Đà Lạt, 2013 The random forests algorithm proposed by Breiman is an ensemble-based approach with very high accuracy. The learning and classification tasks of a set of decision trees take a lot of time, make it intractable when dealing with very large datasets.
Do Thanh Nghi +3 more
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