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 …
[HTML][HTML] Machine learning for Internet of Things data analysis: A survey
Rapid developments in hardware, software, and communication technologies have
facilitated the emergence of Internet-connected sensory devices that provide observations …
facilitated the emergence of Internet-connected sensory devices that provide observations …
Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code
Species distribution modeling (SDM) is widely used in ecology and conservation. Currently,
the most available data for SDM are species presence‐only records (available through …
the most available data for SDM are species presence‐only records (available through …
Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery
P Thanh Noi, M Kappas - Sensors, 2017 - mdpi.com
In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-
Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost …
Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost …
Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots
Root-associated microbes play a key role in plant performance and productivity, making
them important players in agroecosystems. So far, very few studies have assessed the …
them important players in agroecosystems. So far, very few studies have assessed the …
An assessment of the effectiveness of a random forest classifier for land-cover classification
Land cover monitoring using remotely sensed data requires robust classification methods
which allow for the accurate map** of complex land cover and land use categories …
which allow for the accurate map** of complex land cover and land use categories …
Species distribution models: ecological explanation and prediction across space and time
Species distribution models (SDMs) are numerical tools that combine observations of
species occurrence or abundance with environmental estimates. They are used to gain …
species occurrence or abundance with environmental estimates. They are used to gain …
A working guide to boosted regression trees
Summary 1 Ecologists use statistical models for both explanation and prediction, and need
techniques that are flexible enough to express typical features of their data, such as …
techniques that are flexible enough to express typical features of their data, such as …
Random forests for classification in ecology
Classification procedures are some of the most widely used statistical methods in ecology.
Random forests (RF) is a new and powerful statistical classifier that is well established in …
Random forests (RF) is a new and powerful statistical classifier that is well established in …
[HTML][HTML] Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables
Random forest and similar Machine Learning techniques are already used to generate
spatial predictions, but spatial location of points (geography) is often ignored in the modeling …
spatial predictions, but spatial location of points (geography) is often ignored in the modeling …