SAS Scripting Wrapper for Analytics Transfer (SWAT), a powerful Python interface, enables you to integrate your Python code with SAS Cloud Analytic Services (CAS). Using SWAT, you can execute CAS analytic actions, including feature engineering, machine learning modeling, and model testing, and then analyze the results locally. This article demonstrates how you can predict the survival rates of Titanic passengers with a combination of both Python and CAS using SWAT. You can then see how well the models performed with some visual statistics. After you install and configure these resources, start a Jupyter Notebook session to get started!

Accurate predictions of reactive mixing are critical for many Earth and environmental science problems. To investigate mixing dynamics over time under different scenarios, a high-fidelity, finite-element-based numerical model is built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations are performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast, and molecular diffusion. Outputs comprise concentration profiles of the reactants and products. The inputs and outputs of these simulations are concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) emulators to approximate system behavior. The 20 ML emulators based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptron (MLP), are compared to assess these models. The ML emulators are specifically trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay, and degree of mixing. Linear classifiers and regressors fail to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP accurately classify the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predict the QoIs. At run time, trained ML emulators are $\approx10^5$ times faster than the high-fidelity numerical simulations. Speed and accuracy of the ensemble and MLP models facilitate uncertainty quantification, which usually requires 1,000s of model run, to estimate the uncertainty bounds on the QoIs.

We propose self-adaptive training---a new training algorithm that dynamically corrects problematic training labels by model predictions without incurring extra computational cost---to improve generalization of deep learning for potentially corrupted training data. This problem is crucial towards robustly learning from data that are corrupted by, e.g., label noises and out-of-distribution samples. The standard empirical risk minimization (ERM) for such data, however, may easily overfit noises and thus suffers from sub-optimal performance. In this paper, we observe that model predictions can substantially benefit the training process: self-adaptive training significantly improves generalization over ERM under various levels of noises, and mitigates the overfitting issue in both natural and adversarial training. We evaluate the error-capacity curve of self-adaptive training: the test error is monotonously decreasing w.r.t. model capacity. This is in sharp contrast to the recently-discovered double-descent phenomenon in ERM which might be a result of overfitting of noises. Experiments on CIFAR and ImageNet datasets verify the effectiveness of our approach in two applications: classification with label noise and selective classification. We release our code at \url{https://github.com/LayneH/self-adaptive-training}.

The complexity of large-scale neural networks can lead to poor understanding of their internal details. We show that this opaqueness provides an opportunity for adversaries to embed unintended functionalities into the network in the form of Trojan horses. Our novel framework hides the existence of a Trojan network with arbitrary desired functionality within a benign transport network. We prove theoretically that the Trojan network's detection is computationally infeasible and demonstrate empirically that the transport network does not compromise its disguise. Our paper exposes an important, previously unknown loophole that could potentially undermine the security and trustworthiness of machine learning.

Background: Depression has become a major health burden worldwide, and effective detection depression is a great public-health challenge. This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Methods: Resting state EEG data was collected from 24 major depressive patients (MDD) and 29 normal controls using 128 channel HydroCel Geodesic Sensor Net (HCGSN). To better identify depression, we extracted different types of EEG features including linear features, nonlinear features and functional connectivity features phase lagging index (PLI) to comprehensively analyze the EEG signals in patients with MDD. And using different feature selection methods and classifiers to evaluate the optimal feature sets. Results: Functional connectivity feature PLI is superior to the linear features and nonlinear features. And when combining all the types of features to classify MDD patients, we can obtain the highest classification accuracy 82.31% using ReliefF feature selection method and logistic regression (LR) classifier. Analyzing the distribution of optimal feature set, it was found that intrahemispheric connection edges of PLI were much more than the interhemispheric connection edges, and the intrahemispheric connection edges had a significant differences between two groups. Conclusion: Functional connectivity feature PLI plays an important role in depression recognition. Especially, intrahemispheric connection edges of PLI might be an effective biomarker to identify depression. And statistic results suggested that MDD patients might exist functional dysfunction in left hemisphere.

Canonical correlation analysis (CCA) has become a key tool for population neuroimaging for allowing investigation of association between many imaging and non-imaging variables. As age, sex and other variables are often a source of variability not of direct interest, previous work has used CCA on residuals from a model that removes these effects, then proceeded directly to permutation inference. We show that a simple permutation test, as typically used to identify significant modes of shared variation on such data adjusted for nuisance variables, produces inflated error rates. The reason is that residualisation introduces dependencies among the observations that violate the exchangeability assumption. Even in the absence of nuisance variables, however, a simple permutation test for CCA also leads to excess error rates for all canonical correlations other than the first. The reason is that a simple permutation scheme does not ignore the variability already explained by canonical variables of lower rank. Here we propose solutions for both problems: in the case of nuisance variables, we show that projecting the residuals to a lower dimensional space where exchangeability holds results in a valid permutation test; for more general cases, with or without nuisance variables, we propose estimating the canonical correlations in a stepwise manner, removing at each iteration the variance already explained. We also discuss how to address the multiplicity of tests via closure, which leads to an admissible test that is not conservative. We also provide a complete algorithm for permutation inference for CCA.

Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improve the statistical performance of credit scoring models. Instead of focusing on the techniques themselves, this paper leverages alternative data sources to enhance both statistical and economic model performance. The study demonstrates how including call networks, in the context of positive credit information, as a new Big Data source has added value in terms of profit by applying a profit measure and profit-based feature selection. A unique combination of datasets, including call-detail records, credit and debit account information of customers is used to create scorecards for credit card applicants. Call-detail records are used to build call networks and advanced social network analytics techniques are applied to propagate influence from prior defaulters throughout the network to produce influence scores. The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC. In terms of profit, the best model is the one built with only calling behavior features. In addition, the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance. The results have an impact in terms of ethical use of call-detail records, regulatory implications, financial inclusion, as well as data sharing and privacy.

In a supervised learning setting, we have a yardstick or plumbline to judge how well we are doing: the response itself. A frequent question in biological and biomedical applications is whether a property of interest (say, disease type, cell type, the prognosis of a patient) can be "predicted", given one or more other properties, called the predictors. Often we are motivated by a situation in which the property to be predicted is unknown (it lies in the future, or is hard to measure), while the predictors are known. The crucial point is that we learn the prediction rule from a set of training data in which the property of interest is also known. Once we have the rule, we can either apply it to new data, and make actual predictions of unknown outcomes; or we can dissect the rule with the aim of better understanding the underlying biology. Compared to unsupervised learning and what we have seen in Chapters 5, 7 and 9, where we do not know what we are looking for or how to decide whether our result is "right", we are on much more solid ground with supervised learning: the objective is clearly stated, and there are straightforward criteria to measure how well we are doing. The central issues in supervised learning151151 Sometimes the term statistical learning is used, more or less exchangeably. Or did our rule indeed pick up some of the pertinent patterns in the system being studied, which will also apply to yet unseen new data? An example for overfitting: two regression lines are fit to data in the \((x, y)\)-plane (black points). We can think of such a line as a rule that predicts the \(y\)-value, given an \(x\)-value. Both lines are smooth, but the fits differ in what is called their bandwidth, which intuitively can be interpreted their stiffness. The blue line seems overly keen to follow minor wiggles in the data, while the orange line captures the general trend but is less detailed. The effective number of parameters needed to describe the blue line is much higher than for the orange line. Also, if we were to obtain additional data, it is likely that the blue line would do a worse job than the orange line in modeling the new data. We'll formalize these concepts –training error and test set error– later in this chapter. Although exemplified here with line fitting, the concept applies more generally to prediction models. See exemplary applications that motivate the use of supervised learning methods.

Google AI today released TensorFlow Constrained Optimization (TFCO), a supervised machine learning library built for training machine learning models on multiple metrics and "optimizing inequality-constrained problems." The library is designed to help address issues like fairness constraints and predictive parity and help machine learning practitioners better understand things like true positive rates on residents of certain countries, or recall illness diagnoses depending on age and gender. In tests with a Wikipedia data set, the library achieved lower false-positive rates when predicting whether a comment on a Wiki is toxic based on race, religion, gender identity, or sexuality, while maintaining similar accuracy rates. TFCO is made to "take into account the societal and cultural factors necessary to satisfy real-world requirements," said Andrew Zaldivar on behalf of the TFCO team today in a Google AI blog post. "The ability to express many fairness goals as rate constraints can help drive progress in the responsible development of machine learning, but it also requires developers to carefully consider the problem they are trying to address," he said.

Many imbalanced classification tasks require a skillful model that predicts a crisp class label, where both classes are equally important. An example of an imbalanced classification problem where a class label is required and both classes are equally important is the detection of oil spills or slicks in satellite images. The detection of a spill requires mobilizing an expensive response, and missing an event is equally expensive, causing damage to the environment. One way to evaluate imbalanced classification models that predict crisp labels is to calculate the separate accuracy on the positive class and the negative class, referred to as sensitivity and specificity. These two measures can then be averaged using the geometric mean, referred to as the G-mean, that is insensitive to the skewed class distribution and correctly reports on the skill of the model on both classes. In this tutorial, you will discover how to develop a model to predict the presence of an oil spill in satellite images and evaluate it using the G-mean metric. Develop an Imbalanced Classification Model to Detect Oil Spills Photo by Lenny K Photography, some rights reserved. In this project, we will use a standard imbalanced machine learning dataset referred to as the "oil spill" dataset, "oil slicks" dataset or simply "oil."