Productive Analytics managers are investigative analytics managers. Top 5 models one should try are (1) XGboost, (2) L1, L2, glmnet regressions, (3) random forest, (4) SVM, (5) regularized NN, if time permits. Usually XGBoost and glmnet (hybrid of L1 and L2), and SVM should be good for most of the purposes that also provides interpretable models, while NN is good for voice/txt/images/videos. - Ask to verify "Be very clear as to which one is "1" or " " - is it target or reference in binary target variable? Also whether confusion matrix takes care of that correctly and hence whether the "key" ratios of row percentages or column percentages . For various internal programming setup in a software, different software or the modeling specific API deals may deal with this differently. Top models will use the leaky data rather than be good general model of the underlying problem. When you are a company providing your data. Reversing an anonymization and obfuscation can result in a privacy breach that you did not expect. It is a problem when you are developing your own predictive models. You may be creating overly optimistic models that are practically useless and cannot be used in production. " The solution is to use proper verification of actual distributional values of validation data set and the inherent data distributions of the features - For more, see: https://lnkd.in/gyWhs2sg

The probability of a drought for a particular region is crucial when making decisions related to agriculture. Forecasting this probability is critical for management and challenging at the same time. The prediction model should consider multiple factors with complex relationships across the region of interest and neighbouring regions. We approach this problem by presenting an end-to-end solution based on a spatio-temporal neural network. The model predicts the Palmer Drought Severity Index (PDSI) for subregions of interest. Predictions by climate models provide an additional source of knowledge of the model leading to more accurate drought predictions. Our model has better accuracy than baseline Gradient boosting solutions, as the $R^2$ score for it is $0.90$ compared to $0.85$ for Gradient boosting. Specific attention is on the range of applicability of the model. We examine various regions across the globe to validate them under different conditions. We complement the results with an analysis of how future climate changes for different scenarios affect the PDSI and how our model can help to make better decisions and more sustainable economics.

The Infinitesimal Jackknife is a general method for estimating variances of parametric models, and more recently also for some ensemble methods. In this paper we extend the Infinitesimal Jackknife to estimate the covariance between any two models. This can be used to quantify uncertainty for combinations of models, or to construct test statistics for comparing different models or ensembles of models fitted using the same training dataset. Specific examples in this paper use boosted combinations of models like random forests and M-estimators. We also investigate its application on neural networks and ensembles of XGBoost models. We illustrate the efficacy of variance estimates through extensive simulations and its application to the Beijing Housing data, and demonstrate the theoretical consistency of the Infinitesimal Jackknife covariance estimate.

Learning-to-rank, a machine learning technique widely used in information retrieval, has recently been applied to the problem of ligand-based virtual screening, to accelerate the early stages of new drug development. Ranking prediction models learn based on ordinal relationships, making them suitable for integrating assay data from various environments. Existing studies of rank prediction in compound screening have generally used a learning-to-rank method called RankSVM. However, they have not been compared with or validated against the gradient boosting decision tree (GBDT)-based learning-to-rank methods that have gained popularity recently. Furthermore, although the ranking metric called Normalized Discounted Cumulative Gain (NDCG) is widely used in information retrieval, it only determines whether the predictions are better than those of other models. In other words, NDCG is incapable of recognizing when a prediction model produces worse than random results. Nevertheless, NDCG is still used in the performance evaluation of compound screening using learning-to-rank. This study used the GBDT model with ranking loss functions, called lambdarank and lambdaloss, for ligand-based virtual screening; results were compared with existing RankSVM methods and GBDT models using regression. We also proposed a new ranking metric, Normalized Enrichment Discounted Cumulative Gain (NEDCG), which aims to properly evaluate the goodness of ranking predictions. Results showed that the GBDT model with learning-to-rank outperformed existing regression methods using GBDT and RankSVM on diverse datasets. Moreover, NEDCG showed that predictions by regression were comparable to random predictions in multi-assay, multi-family datasets, demonstrating its usefulness for a more direct assessment of compound screening performance.

Predicting the probability of non-performing loans for individuals has a vital and beneficial role for banks to decrease credit risk and make the right decisions before giving the loan. The trend to make these decisions are based on credit study and in accordance with generally accepted standards, loan payment history, and demographic data of the clients. In this work, we evaluate how different Machine learning models such as Random Forest, Decision tree, KNN, SVM, and XGBoost perform on the dataset provided by a private bank in Ethiopia. Further, motivated by this evaluation we explore different feature selection methods to state the important features for the bank. Our findings show that XGBoost achieves the highest F1 score on the KMeans SMOTE over-sampled data. We also found that the most important features are the age of the applicant, years of employment, and total income of the applicant rather than collateral-related features in evaluating credit risk. Work done when the authors were a research intern at Chapa. Equally contributed to this work.

Accurate prediction of crop yield before harvest is of great importance for crop logistics, market planning, and food distribution around the world. Yield prediction requires monitoring of phenological and climatic characteristics over extended time periods to model the complex relations involved in crop development. Remote sensing satellite images provided by various satellites circumnavigating the world are a cheap and reliable way to obtain data for yield prediction. The field of yield prediction is currently dominated by Deep Learning approaches. While the accuracies reached with those approaches are promising, the needed amounts of data and the ``black-box'' nature can restrict the application of Deep Learning methods. The limitations can be overcome by proposing a pipeline to process remote sensing images into feature-based representations that allow the employment of Extreme Gradient Boosting (XGBoost) for yield prediction. A comparative evaluation of soybean yield prediction within the United States shows promising prediction accuracies compared to state-of-the-art yield prediction systems based on Deep Learning. Feature importances expose the near-infrared spectrum of light as an important feature within our models. The reported results hint at the capabilities of XGBoost for yield prediction and encourage future experiments with XGBoost for yield prediction on other crops in regions all around the world.

Machine learning has opened up new tools for financial fraud detection. Using a sample of annotated transactions, a machine learning classification algorithm learns to detect frauds. With growing credit card transaction volumes and rising fraud percentages there is growing interest in finding appropriate machine learning classifiers for detection. However, fraud data sets are diverse and exhibit inconsistent characteristics. As a result, a model effective on a given data set is not guaranteed to perform on another. Further, the possibility of temporal drift in data patterns and characteristics over time is high. Additionally, fraud data has massive and varying imbalance. In this work, we evaluate sampling methods as a viable pre-processing mechanism to handle imbalance and propose a data-driven classifier selection strategy for characteristic highly imbalanced fraud detection data sets. The model derived based on our selection strategy surpasses peer models, whilst working in more realistic conditions, establishing the effectiveness of the strategy.

Mechanical exfoliation of graphene and its identification by optical inspection is one of the milestones in condensed matter physics that sparked the field of 2D materials. Finding regions of interest from the entire sample space and identification of layer number is a routine task potentially amenable to automatization. We propose supervised pixel-wise classification methods showing a high performance even with a small number of training image datasets that require short computational time without GPU. We introduce four different tree-based machine learning algorithms -- decision tree, random forest, extreme gradient boost, and light gradient boosting machine. We train them with five optical microscopy images of graphene, and evaluate their performances with multiple metrics and indices. We also discuss combinatorial machine learning models between the three single classifiers and assess their performances in identification and reliability. The code developed in this paper is open to the public and will be released at github.com/gjung-group/Graphene_segmentation.

SARS-COV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdown, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in-silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure Covid-19. We employed the Lipinski rules on the retrieved molecules from the ChEMBL database and found 133 drug-likeness bioactive molecules against SARS coronavirus 3CL Protease. Based on standard IC50, the dataset was divided into three classes active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR) based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting, XGBoost, Support Vector, Decision Tree, and Random Forest based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134 and 426898. These molecules are highly suitable drug candidates for SARS-COV-2 3CL Protease. In the next step, the efficacy of bioactive molecules is computed in terms of binding affinity using molecular docking and then shortlisted six bioactive molecules with ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-COV-2. It is anticipated that the pharmacologist/drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-COV-2. They can adopt these promising compounds for their downstream drug development stages.

Learning from imbalanced data is a challenging task. Standard classification algorithms tend to perform poorly when trained on imbalanced data. Some special strategies need to be adopted, either by modifying the data distribution or by redesigning the underlying classification algorithm to achieve desirable performance. The prevalence of imbalance in real-world datasets has led to the creation of a multitude of strategies for the class imbalance issue. However, not all the strategies are useful or provide good performance in different imbalance scenarios. There are numerous approaches to dealing with imbalanced data, but the efficacy of such techniques or an experimental comparison among those techniques has not been conducted. In this study, we present a comprehensive analysis of 26 popular sampling techniques to understand their effectiveness in dealing with imbalanced data. Rigorous experiments have been conducted on 50 datasets with different degrees of imbalance to thoroughly investigate the performance of these techniques. A detailed discussion of the advantages and limitations of the techniques, as well as how to overcome such limitations, has been presented. We identify some critical factors that affect the sampling strategies and provide recommendations on how to choose an appropriate sampling technique for a particular application.