Joints in Random Forests
Authors: Alvaro Correia, Robert Peharz, Cassio P. de Campos
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that our models often outperform common routines to treat missing data, such as K-nearest neighbour imputation, and moreover, that our models can naturally detect outliers by monitoring the marginal probability of input features. |
| Researcher Affiliation | Collaboration | Alvaro H. C. Correia EMAIL Eindhoven University of Technology Robert Peharz EMAIL Eindhoven University of Technology Cassio de Campos EMAIL Eindhoven University of Technology ... During part of the three years prior to the submission of this work, the authors were affiliated with the following institutions besides TU Eindhoven: Alvaro Correia was a full-time employee at Accenture and Itaú-Unibanco, and affiliated with Utrecht University; Cassio de Campos was affiliated with Queen s University Belfast and Utrecht University; Robert Peharz was affiliated with the University of Cambridge. |
| Pseudocode | Yes | Algorithm 1: Converting DT to PC (Ge DT). |
| Open Source Code | No | The paper does not provide an explicit statement about the release of their source code (e.g., 'Our code is available at...') nor does it provide a direct link to a code repository for the implemented methodology. It refers to 'Learn SPN [16]' which is a prominent PC learner, implying its use, but not releasing their specific implementation. |
| Open Datasets | Yes | We compare the accuracy of the methods in a selection of datasets from the Open ML-CC18 benchmark3 [51] and the wine-quality dataset [33]. ... We repeat a similar experiment with images, where we use the MNIST dataset [27] to fit a Gaussian KDE, a Random Forest and its corresponding Ge F+. We then evaluate these models on different digit datasets, namely Semeion [11] and SVHN [34] (converted to grayscale and 784 pixels)... |
| Dataset Splits | Yes | Table 1 presents results for 30% of missing values at test time (different percentages are shown in the supp. material), with 95% confidence intervals across 10 repetitions of 5-fold cross-validation. ... We then compute the log-density of unseen data (70/30 train test split) for the two wine types with both models. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions software tools like 'Learn SPN' but does not provide specific version numbers for any software dependencies, which would be required for reproducible setup. |
| Experiment Setup | Yes | In all experiments, Ge F, Ge F(Learn SPN) and the RF share the exact same structure (partition over the feature space) and are composed of 100 trees; including more trees has been shown to yield only marginal gains in most cases [39]. In Ge F(Learn SPN), we run Learn SPN only for leaves with more than 30 samples, defaulting to a fully factorised model in smaller leaves. |