Explaining Support Vector Machines: A Color Based Nomogram.

PROBLEM SETTING: Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models. OBJECTIVE: In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto) not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables. RESULTS: Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant). When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable. CONCLUSIONS: This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method

Agrobacterium tumefaciens C58 presence affects Bacillus velezensis 32a ecological fitness in the tomato rhizosphere

International audienceThe persistence of pathogenic Agrobacterium strains as soil-associated saprophytes may cause an inconsistency in the efficacy of the biocontrol inoculants under field condition. The study of the interaction occurring in the rhizosphere between the beneficial and the pathogenic microbes is thus interesting for the development of effective biopesticides for the management of crown gall disease. However, very little is still known about the influence of these complex interactions on the biocontrol determinants of beneficial bacteria, especially Bacillus strains. This study aimed to evaluate the effect of the soil borne pathogen Agrobacterium tumefaciens C58 on root colonization and lipopeptide production by Bacillus velezensis strain 32a during interaction with tomato plants. Results show that the presence of A. tumefaciens C58 positively impacted the root colonization level of the Bacillus strain. However, negative impact on surfactin production was observed in Agrobacterium-treated seedling, compared with control. Further investigation suggests that these modulations are due to a modified tomato root exudate composition during the tripartite interaction. Thus, this work contributes to enhance the knowledge on the impact of interspecies interaction on the ecological fitness of Bacillus cells living in the rhizosphere