This study explores an interesting pattern emerging from research that combines artificial intelligence with sound symbolism. In these studies, supervised machine learning algorithms are trained to classify samples based on the sounds of referent names. Machine learning algorithms are efficient learners of sound symbolism, but they tend to bias one category over the other. The pattern is this: when a category arguably represents greater threat, the algorithms tend to overpredict to that category. A hypothesis, framed by error management theory, is presented that proposes that this may be evidence of an adaptation to preference cautious behaviour. This hypothesis is tested by constructing extreme gradient boosted (XGBoost) models using the sounds that make up the names of Chinese, Japanese and Korean Pokemon and observing classification error distribution.

Over the past few years, the use of machine learning models has emerged as a generic and powerful means for prediction purposes. At the same time, there is a growing demand for interpretability of prediction models. To determine which features of a dataset are important to predict a target variable $Y$, a Feature Importance (FI) method can be used. By quantifying how important each feature is for predicting $Y$, irrelevant features can be identified and removed, which could increase the speed and accuracy of a model, and moreover, important features can be discovered, which could lead to valuable insights. A major problem with evaluating FI methods, is that the ground truth FI is often unknown. As a consequence, existing FI methods do not give the exact correct FI values. This is one of the many reasons why it can be hard to properly interpret the results of an FI method. Motivated by this, we introduce a new global approach named the Berkelmans-Pries FI method, which is based on a combination of Shapley values and the Berkelmans-Pries dependency function. We prove that our method has many useful properties, and accurately predicts the correct FI values for several cases where the ground truth FI can be derived in an exact manner. We experimentally show for a large collection of FI methods (468) that existing methods do not have the same useful properties. This shows that the Berkelmans-Pries FI method is a highly valuable tool for analyzing datasets with complex interdependencies.

XGBoost, LightGBM, or CatBoost are libraries that share (by default) the same kind of underlying model: decision trees. These decision trees are combined iteratively, using Gradient Boosting. I.e. the addition of new nodes to the current tree is done so that a non-linear objective, usually the squared error, is optimized. To handle the non-linearity, the objective is linearized using its Gradient and Hessian. As a reminder, the prediction process is relatively simple: given a row of data, each decision tree of the ensemble is browsed.

The effect is that the predictions, and in turn, prediction errors, made by each tree in the ensemble are more different or less correlated. When the predictions from these less correlated trees are averaged to make a prediction, it often results in better performance than bagged decision trees. Perhaps the most important hyperparameter to tune for the random forest is the number of random features to consider at each split point. Random forests' tuning parameter is the number of randomly selected predictors, k, to choose from at each split, and is commonly referred to as mtry. In the regression context, Breiman (2001) recommends setting mtry to be one-third of the number of predictors.

This study constructs machine learning algorithms that are trained to classify samples using sound symbolism, and then it reports on an experiment designed to measure their understanding against human participants. Random forests are trained using the names of Pokemon, which are fictional video game characters, and their evolutionary status. Pokemon undergo evolution when certain in-game conditions are met. Evolution changes the appearance, abilities, and names of Pokemon. In the first experiment, we train three random forests using the sounds that make up the names of Japanese, Chinese, and Korean Pokemon to classify Pokemon into pre-evolution and post-evolution categories. We then train a fourth random forest using the results of an elicitation experiment whereby Japanese participants named previously unseen Pokemon. In Experiment 2, we reproduce those random forests with name length as a feature and compare the performance of the random forests against humans in a classification experiment whereby Japanese participants classified the names elicited in Experiment 1 into pre-and post-evolution categories. Experiment 2 reveals an issue pertaining to overfitting in Experiment 1 which we resolve using a novel cross-validation method. The results show that the random forests are efficient learners of systematic sound-meaning correspondence patterns and can classify samples with greater accuracy than the human participants.

We show that regression predictions from linear and tree-based models can be represented as linear combinations of target instances in the training data. This also holds for models constructed as ensembles of trees, including Random Forests and Gradient Boosting Machines. The weights used in these linear combinations are measures of instance importance, complementing existing measures of feature importance, such as SHAP and LIME. We refer to these measures as AXIL weights (Additive eXplanations with Instance Loadings). Since AXIL weights are additive across instances, they offer both local and global explanations. Our work contributes to the broader effort to make machine learning predictions more interpretable and explainable.

Mango is a python library to find the optimal hyperparameters for machine learning classifiers. Mango is straightforward to use. Following example minimizes the quadratic function whose input is an integer between -10 and 10. Note that best parameters may be different but accuracy should be 0.9315. More examples are available in the examples directory (Facebook's Prophet, XGBoost, SVM).

Dataset scaling, also known as normalization, is an essential preprocessing step in a machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary within the same range. This transformation is known to improve the performance of classification models, but there are several scaling techniques to choose from, and this choice is not generally done carefully. In this paper, we execute a broad experiment comparing the impact of 5 scaling techniques on the performances of 20 classification algorithms among monolithic and ensemble models, applying them to 82 publicly available datasets with varying imbalance ratios. Results show that the choice of scaling technique matters for classification performance, and the performance difference between the best and the worst scaling technique is relevant and statistically significant in most cases. They also indicate that choosing an inadequate technique can be more detrimental to classification performance than not scaling the data at all. We also show how the performance variation of an ensemble model, considering different scaling techniques, tends to be dictated by that of its base model. Finally, we discuss the relationship between a model's sensitivity to the choice of scaling technique and its performance and provide insights into its applicability on different model deployment scenarios. Full results and source code for the experiments in this paper are available in a GitHub repository.\footnote{https://github.com/amorimlb/scaling\_matters}

Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

The risk of bankruptcy in a publicly traded firm is of major interest to shareholders, creditors, and employees. Prior literature has investigated the predictive performance of different forecasting models, mainly the discriminant analysis with accounting information (Altman, 1968), the distance to default structural model (Bharath and Shumway, 2008), and the hazard model with accounting and market information (Shumway, 2001; Chava and Jarrow, 2004). In this paper we investigate the benefits of applying high dimensional machine learning (ML) methods to bankruptcy prediction. We use a comprehensive sample of bankruptcies for U.S. publicly traded companies from 1969 to 2019 with financial, market, macro, and text based predictors. We study the performance of eight ML algorithms: the hazard model of Shumway (2001) and Chava and Jarrow (2004) enhanced with a penalty function (LASSO and Ridge), bagged trees (random forest and survival random forest), gradient boosted trees (XG Boost and LightGBM), and two specifications of neural networks (one shallower and one deeper).