The successes achieved by deep neural networks in computer vision tasks have led in recent years to the emergence of a new research area dubbed Multi-Dimensional Encoding (MDE). Methods belonging to this family aim to transform tabular data into a homogeneous form of discrete digital signals (images) to apply convolutional networks to initially unsuitable problems. Despite the successive emerging works, the pool of multi-dimensional encoding methods is still low, and the scope of research on existing modality encoding techniques is quite limited. To contribute to this area of research, we propose the Radar-based Encoding from Tabular to Image REpresentation (RETIRE), which allows tabular data to be represented as radar graphs, capturing the feature characteristics of each problem instance. RETIRE was compared with a pool of state-of-the-art MDE algorithms as well as with XGBoost in terms of classification accuracy and computational complexity. In addition, an analysis was carried out regarding transferability and explainability to provide more insight into both RETIRE and existing MDE techniques. The results obtained, supported by statistical analysis, confirm the superiority of RETIRE over other established MDE methods.

This study examines the effect that different feature selection methods have on models created with XGBoost, a popular machine learning algorithm with superb regularization methods. It shows that three different ways for reducing the dimensionality of features produces no statistically significant change in the prediction accuracy of the model. This suggests that the traditional idea of removing the noisy training data to make sure models do not overfit may not apply to XGBoost. But it may still be viable in order to reduce computational complexity.

Recent papers have used machine learning architecture to fit low-order functional ANOVA models with main effects and second-order interactions. These GAMI (GAM + Interaction) models are directly interpretable as the functional main effects and interactions can be easily plotted and visualized. Unfortunately, it is not easy to incorporate the monotonicity requirement into the existing GAMI models based on boosted trees, such as EBM (Lou et al. 2013) and GAMI-Lin-T (Hu et al. 2022). This paper considers models of the form $f(x)=\sum_{j,k}f_{j,k}(x_j, x_k)$ and develops monotone tree-based GAMI models, called monotone GAMI-Tree, by adapting the XGBoost algorithm. It is straightforward to fit a monotone model to $f(x)$ using the options in XGBoost. However, the fitted model is still a black box. We take a different approach: i) use a filtering technique to determine the important interactions, ii) fit a monotone XGBoost algorithm with the selected interactions, and finally iii) parse and purify the results to get a monotone GAMI model. Simulated datasets are used to demonstrate the behaviors of mono-GAMI-Tree and EBM, both of which use piecewise constant fits. Note that the monotonicity requirement is for the full model. Under certain situations, the main effects will also be monotone. But, as seen in the examples, the interactions will not be monotone.

When I was just starting on my quest to understand Machine Learning algorithms, I would get overwhelmed with all the math-y stuff. I found it difficult to understand the math behind an algorithm without fully grasping the intuition. So I would gravitate towards sources that completely broke down the algorithm into simple steps and made it digestible to someone who never even heard the word Algorithm before. Okay, that is a blatant exaggeration, but you know what I mean. So that's what I'm attempting to do now.

One simple method is Permutation Feature Importance, It is a model inspection technique that can be used for any fitted estimator when the data is tabular. The permutation feature importance is defined to be the decrease in a model score when a single feature value is randomly shuffled. This procedure breaks the relationship between the feature and the target, thus the drop in the model score is indicative of how much the model depends on the feature. A good practice is to drop one of the correlated features based on domain understanding and try to apply the Permutation Feature Importance algorithm which will provide better feature understanding. Let's discuss another method to interpret the black box models.

Decision Tree: Every hiring manager has a set of criteria such as education level, number of years of experience, interview performance. A decision tree is analogous to a hiring manager interviewing candidates based on his or her own criteria. Bagging: Now imagine instead of a single interviewer, now there is an interview panel where each interviewer has a vote. Bagging or bootstrap aggregating involves combining inputs from all interviewers for the final decision through a democratic voting process. Random Forest: It is a bagging-based algorithm with a key difference wherein only a subset of features is selected at random.

XGBoost stands for "Extreme Gradient Boosting". XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements Machine Learning algorithms under the Gradient Boosting framework. It provides a parallel tree boosting to solve many data science problems in a fast and accurate way. Boosting is an ensemble learning technique to build a strong classifier from several weak classifiers in series. Boosting algorithms play a crucial role in dealing with bias-variance trade-off.

In recent times, ensemble techniques have become popular among data scientists and enthusiasts. Until now Random Forest and Gradient Boosting algorithms were winning the data science competitions and hackathons, over the period of the last few years XGBoost has been performing better than other algorithms on problems involving structured data. Apart from its performance, XGBoost is also recognized for its speed, accuracy and scale. XGBoost is developed on the framework of Gradient Boosting. Just like other boosting algorithms XGBoost uses decision trees for its ensemble model.

The proposed algorithms are based on the XGBoost algorithm, which is known as one of the most powerful algorithms in the machine learning literature. Our main idea is to model the conditional mean of clinical outcome or the decision rule via additive regression trees, and use the boosting technique to estimate each single tree iteratively. Our approaches overcome the challenge of correct model specification, which is required in current parametric methods. The major contribution of our proposed algorithms is providing efficient and accurate estimation of the highly nonlinear and complex optimal individualized treatment rules that often arise in practice. Finally, we illustrate the superior performance of our algorithms by extensive simulation studies and conclude with an application to the real data from a diabetes Phase III trial. 1 Introduction Precision medicine, as an emerging medical approach for disease treatment and prevention, has received more and more attention among government, healthcare industry and academia in recent years. It is a well-known fact that there exists a significant heterogeneity for patients in response to treatments. For example, as demonstrated in [9], for patients who are infected with human immunodeficiency virus and tuberculosis, their optimal timing of antiretroviral therapy (ART) varies significantly.

XGBoost was originally developed by Tianqi Chen in his paper titeled "XGBoost: A Scalable Tree Boosting System." XGBoost itself is an enhancement to the gradient boosting algorithm created by Jerome H. Friedman in his paper titled "Greedy Function Approximation: A Gradient Boosting Machine." Both papers are well worth exploring.