Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms
Background Organoids are 3-dimensional experimental models that summarize the
anatomical and functional structure of an organ. Although a promising experimental model …
anatomical and functional structure of an organ. Although a promising experimental model …
Can't see the forest for the trees: Analyzing groves to explain random forests
G Szepannek, BH Holt - Behaviormetrika, 2024 - Springer
Random forests are currently one of the most popular algorithms for supervised machine
learning tasks. By taking into account for many trees instead of a single one the resulting …
learning tasks. By taking into account for many trees instead of a single one the resulting …
How do applied researchers use the Causal Forest? A methodological review of a method
P Rehill - arXiv preprint arXiv:2404.13356, 2024 - arxiv.org
This paper conducts a methodological review of papers using the causal forest machine
learning method for flexibly estimating heterogeneous treatment effects. It examines 133 …
learning method for flexibly estimating heterogeneous treatment effects. It examines 133 …
Identification of representative trees in random forests based on a new tree-based distance measure
In life sciences, random forests are often used to train predictive models. However, gaining
any explanatory insight into the mechanics leading to a specific outcome is rather complex …
any explanatory insight into the mechanics leading to a specific outcome is rather complex …
Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis
J Zeng, M Zhang, J Du, J Han, Q Song… - Frontiers in …, 2024 - frontiersin.org
Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure,
but also have many comorbidities, which can be life-threatening and require timely …
but also have many comorbidities, which can be life-threatening and require timely …
[HTML][HTML] Bayesian CART models for insurance claims frequency
The accuracy and interpretability of a (non-life) insurance pricing model are essential
qualities to ensure fair and transparent premiums for policy-holders, that reflect their risk. In …
qualities to ensure fair and transparent premiums for policy-holders, that reflect their risk. In …
Distilling interpretable causal trees from causal forests
P Rehill - arXiv preprint arXiv:2408.01023, 2024 - arxiv.org
Machine learning methods for estimating treatment effect heterogeneity promise greater
flexibility than existing methods that test a few pre-specified hypotheses. However, one …
flexibility than existing methods that test a few pre-specified hypotheses. However, one …
Construction of Artificial Most Representative Trees by Minimizing Tree-Based Distance Measures
The random forest (RF) algorithm is known for its predictive performance but has been
criticized for its lack of interpretability due to its complex ensemble nature. To address the …
criticized for its lack of interpretability due to its complex ensemble nature. To address the …
Interpretable machine learning for survival analysis
With the spread and rapid advancement of black box machine learning models, the field of
interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become …
interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become …
Enhancing Classification on Disease Diagnosis with Deep Learning
S Sharna - 2024 - rave.ohiolink.edu
The use of statistical and machine learning methods in collection, evaluation and
presentation of biological data is very extensive. This reflects a need for precise quantitative …
presentation of biological data is very extensive. This reflects a need for precise quantitative …