The ROC curve is also used in order to compute the area under the ROC curve metric. The ROC curve of a perfect model will approach the top-left corner, whilst a random model will approach the diagonal (True positive rate False positive rate). The area under the ROC curve ranges between 0. and 1 and can be computed via a BinaryClassificationEvaluator object The result is impressive, despite the attempt to hamper the model quality. The area under the ROC curve for the training set can be obtained from the model summary lr_model.summary.areaUnderROC. The BinaryClassificationEvaluator object can also be used to compute the area under the PR curve.

Python has become the most popular data science and machine learning programming language. But in order to obtain effective data and results, it's important that you have a basic understanding of how it works with machine learning. In this introductory tutorial, you'll learn the basics of Python for machine learning, including different model types and the steps to take to ensure you obtain quality data, using a sample machine learning problem. In addition, you'll get to know some of the most popular libraries and tools for machine learning. Machine learning (ML) is a form of artificial intelligence (AI) that teaches computers to make predictions and recommendations and solve problems based on data. Its problem-solving capabilities make it a useful tool in industries such as financial services, healthcare, marketing and sales, and education among others. There are three main types of machine learning: supervised, unsupervised, and reinforcement. In supervised learning, the computer is given a set of training data that includes both the input data (what we want to predict) and the output data (the prediction).

Jet tagging is a critical yet challenging classification task in particle physics. While deep learning has transformed jet tagging and significantly improved performance, the lack of a large-scale public dataset impedes further enhancement. In this work, we present JetClass, a new comprehensive dataset for jet tagging. The JetClass dataset consists of 100 M jets, about two orders of magnitude larger than existing public datasets. A total of 10 types of jets are simulated, including several types unexplored for tagging so far. Based on the large dataset, we propose a new Transformer-based architecture for jet tagging, called Particle Transformer (ParT). By incorporating pairwise particle interactions in the attention mechanism, ParT achieves higher tagging performance than a plain Transformer and surpasses the previous state-of-the-art, ParticleNet, by a large margin. The pre-trained ParT models, once fine-tuned, also substantially enhance the performance on two widely adopted jet tagging benchmarks. The dataset, code and models are publicly available at https://github.com/jet-universe/particle_transformer.

Model evaluation is an important part of a data science project and it's exactly this part that quantifies how good your model is, how much it has improved from the previous version, how much better it is than your colleague's model, and how much room for improvement there still is. It is not unusual in machine learning applications to deal with imbalanced datasets such as fraud detection, computer network intrusion, medical diagnostics, and many more. Data imbalance refers to unequal distribution of classes within a dataset, namely that there are far fewer events in one class in comparison to the others. If, for example we have credit card fraud detection dataset, most of the transactions are not fraudulent and very few can be classed as fraud detections. This underrepresented class is called the minority class, and by convention, the positive class.

With machine learning being a popular topic in current computational materials science literature, creating representations for compounds has become common place. These representations are rarely compared, as evaluating their performance - and the performance of the algorithms that they are used with - is non-trivial. With many materials datasets containing bias and skew caused by the research process, leave one cluster out cross validation (LOCO-CV) has been introduced as a way of measuring the performance of an algorithm in predicting previously unseen groups of materials. This raises the question of the impact, and control, of the range of cluster sizes on the LOCO-CV measurement outcomes. We present a thorough comparison between composition-based representations, and investigate how kernel approximation functions can be used to better separate data to enhance LOCO-CV applications. We find that domain knowledge does not improve machine learning performance in most tasks tested, with band gap prediction being the notable exception. We also find that the radial basis function improves the linear separability of chemical datasets in all 10 datasets tested and provide a framework for the application of this function in the LOCO-CV process to improve the outcome of LOCO-CV measurements regardless of machine learning algorithm, choice of metric, and choice of compound representation. We recommend kernelised LOCO-CV as a training paradigm for those looking to measure the extrapolatory power of an algorithm on materials data.

Despite recent advances in algorithmic fairness, To address these issues there has recently been a significant methodologies for achieving fairness with generalized body of work in the machine learning community on linear models (GLMs) have yet to be algorithmic fairness in the context of predictive modeling, explored in general, despite GLMs being widely including (i) data preprocessing methods that try to reduce used in practice. In this paper we introduce two disparities, (ii) in-process approaches which enforce fairness fairness criteria for GLMs based on equalizing during model training, and (iii) post-process approaches expected outcomes or log-likelihoods. We prove which adjust a model's predictions to achieve fairness after that for GLMs both criteria can be achieved via training is completed. However, the majority of this work a convex penalty term based solely on the linear has focused on classification problems with binary outcome components of the GLM, thus permitting efficient variables, and to a lesser extent on regression.

In this paper we investigate Charon's craters size distribution using a deep learning model. This is motivated by the recent results of Singer et al. (2019) who, using manual cataloging, found a change in the size distribution slope of craters smaller than 12 km in diameter, translating into a paucity of small Kuiper Belt objects. These results were corroborated by Robbins and Singer (2021), but opposed by Morbidelli et al. (2021), necessitating an independent review. Our MaskRCNN-based ensemble of models was trained on Lunar, Mercurian, and Martian crater catalogues and both optical and digital elevation images. We use a robust image augmentation scheme to force the model to generalize and transfer-learn into icy objects. With no prior bias or exposure to Charon, our model find best fit slopes of q =-1.47+-0.33 for craters smaller than 10 km, and q =-2.91+-0.51 for craters larger than 15 km. These values indicate a clear change in slope around 15 km as suggested by Singer et al. (2019) and thus independently confirm their conclusions. Our slopes however are both slightly flatter than those found more recently by Robbins and Singer (2021). Our trained models and relevant codes are available online on github.com/malidib/ACID .

This research assumes that you are familiar with class imbalance and the ADASYN algorithm. We strongly encourage our readers to review the conference article that launched ADASYN (just type that into Google Scholar or see the References section of this document), and then read any number of articles in Towards Data Science that discuss class imbalance and ADASYN. Because this is neither a guide nor an overview; it is voyage into uncharted waters with startling discoveries. The answers are 1) surprising, 2) fascinating, and 3) extraordinary, in that order. All models in this research were conducted using the RandomForest and LogisticRegression algorithms in the sci-kit learn library to gain information about both tree and linear structures, respectively. All predictive models were 10-fold cross-validated with stratified sampling using "stratify y" in train_test_split and "cv 10" in GridSearchCV.

Nonlinear wave interactions, such as shock refraction at an inclined density interface, in magnetohydrodynamic (MHD) lead to a plethora of wave patterns with myriad wave types. Identification of different types of MHD waves is an important and challenging task in such complex wave patterns. Moreover, owing to the multiplicity of solutions and their admissibility for different systems, especially for intermediate-type MHD shock waves, the identification of MHD wave types is complicated if one solely relies on the Rankine-Hugoniot jump conditions. MHD wave detection is further exacerbated by the unphysical smearing of discontinuous shock waves in numerical simulations. We present two MHD wave detection methods based on a convolutional neural network (CNN) which enables the classification of waves and identification of their locations. The first method separates the output into a regression (location prediction) and a classification problem assuming the number of waves for each training data is fixed. In the second method, the number of waves is not specified a priori and the algorithm, using only regression, predicts the waves' locations and classifies their types. The first fixed output model efficiently provides high precision and recall, the accuracy of the entire neural network achieved is up to 0.99, and the classification accuracy of some waves approaches unity. The second detection model has relatively lower performance, with more sensitivity to the setting of parameters, such as the number of grid cells N_{grid} and the thresholds of confidence score and class probability, etc. The proposed two methods demonstrate very strong potential to be applied for MHD wave detection in some complex wave structures and interactions.

In a worldwide health crisis as exigent as COVID-19, there has become a pressing need for rapid, reliable diagnostics. Currently, popular testing methods such as reverse transcription polymerase chain reaction (RT-PCR) can have high false negative rates. Consequently, COVID-19 patients are not accurately identified nor treated quickly enough to prevent transmission of the virus. However, the recent rise of medical CT data has presented promising avenues, since CT manifestations contain key characteristics indicative of COVID-19. This study aimed to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. First, the dataset utilized in this study was derived from three major sources, comprising a total of 17,698 chest CT slices across 923 patient cases. Image preprocessing algorithms were then developed to reduce noise by excluding irrelevant features. Transfer learning was also implemented with the EfficientNetB7 pre-trained model to provide a backbone architecture and save computational resources. Lastly, several explainability techniques were leveraged to qualitatively validate model performance by localizing infected regions and highlighting fine-grained pixel details. The proposed model attained an overall accuracy of 0.927 and a sensitivity of 0.958. Explainability measures showed that the model correctly distinguished between relevant, critical features pertaining to COVID-19 chest CT images and normal controls. Deep learning frameworks provide efficient, human-interpretable COVID-19 diagnostics that could complement radiologist decisions or serve as an alternative screening tool. Future endeavors may provide insight into infection severity, patient risk stratification, and prognosis.