2,717 research outputs found
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
We introduce the C++ application and R package ranger. The software is a fast
implementation of random forests for high dimensional data. Ensembles of
classification, regression and survival trees are supported. We describe the
implementation, provide examples, validate the package with a reference
implementation, and compare runtime and memory usage with other
implementations. The new software proves to scale best with the number of
features, samples, trees, and features tried for splitting. Finally, we show
that ranger is the fastest and most memory efficient implementation of random
forests to analyze data on the scale of a genome-wide association study
Releasing the national economic potential of provincial city-regions: the rational for and implications of a ‘northern way' growth strategy
Methods of utilizing the eighth grade study guide in the third class districts of Yellowstone County Montana
Galilean and Dynamical Invariance of Entanglement in Particle Scattering
Particle systems admit a variety of tensor product structures (TPSs)
depending on the algebra of observables chosen for analysis. Global symmetry
transformations and dynamical transformations may be resolved into local
unitary operators with respect to certain TPSs and not with respect to others.
Symmetry-invariant and dynamical-invariant TPSs are defined and various notions
of entanglement are considered for scattering states.Comment: 4 pages, no figures; v.3 has typos corrected, a new reference, and a
revised conclusio
Block Forests:random forests for blocks of clinical and omics covariate data
Background
In the last years more and more multi-omics data are becoming available, that is, data featuring measurements of several types of omics data for each patient. Using multi-omics data as covariate data in outcome prediction is both promising and challenging due to the complex structure of such data. Random forest is a prediction method known for its ability to render complex dependency patterns between the outcome and the covariates. Against this background we developed five candidate random forest variants tailored to multi-omics covariate data. These variants modify the split point selection of random forest to incorporate the block structure of multi-omics data and can be applied to any outcome type for which a random forest variant exists, such as categorical, continuous and survival outcomes. Using 20 publicly available multi-omics data sets with survival outcome we compared the prediction performances of the block forest variants with alternatives. We also considered the common special case of having clinical covariates and measurements of a single omics data type available.
Results
We identify one variant termed “block forest” that outperformed all other approaches in the comparison study. In particular, it performed significantly better than standard random survival forest (adjusted p-value: 0.027). The two best performing variants have in common that the block choice is randomized in the split point selection procedure. In the case of having clinical covariates and a single omics data type available, the improvements of the variants over random survival forest were larger than in the case of the multi-omics data. The degrees of improvements over random survival forest varied strongly across data sets. Moreover, considering all clinical covariates mandatorily improved the performance. This result should however be interpreted with caution, because the level of predictive information contained in clinical covariates depends on the specific application.
Conclusions
The new prediction method block forest for multi-omics data can significantly improve the prediction performance of random forest and outperformed alternatives in the comparison. Block forest is particularly effective for the special case of using clinical covariates in combination with measurements of a single omics data type
arfpy: A python package for density estimation and generative modeling with adversarial random forests
This paper introduces , a python implementation of
Adversarial Random Forests (ARF) (Watson et al., 2023), which is a lightweight
procedure for synthesizing new data that resembles some given data. The
software equips practitioners with straightforward
functionalities for both density estimation and generative modeling. The method
is particularly useful for tabular data and its competitive performance is
demonstrated in previous literature. As a major advantage over the mostly deep
learning based alternatives, combines the method's reduced
requirements in tuning efforts and computational resources with a user-friendly
python interface. This supplies audiences across scientific fields with
software to generate data effortlessly.Comment: The software is available at https://github.com/bips-hb/arfp
Representing Children and Youth
Quality legal representation of all parties is essential to a high-functioning dependency court process. Quality legal representation of children in particular is essential in obtaining good outcomes for children. An adversarial court process that depends on competing independent advocacy to provide information will not produce good outcomes for litigants who lack competent advocates. Dependency court decisions are as good as the information on which the decisions are based. In order to promote the welfare of children in dependency court, therefore, children must be provided with competent independent legal representation
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