Machine learning algorithms are used in finance, medicine, and criminal justice, and therefore they can have a deep The advent of machine learning techniques has impact on society. With the recent success of AI applications made it possible to obtain predictive systems that in the private and public domain, legal professionals are now have overturned traditional legal practices. However, interested in artificial intelligence, especially since many rather than leading to systems seeking to startups disrupt the legal market space by seeking to benefit replace humans, the search for the determinants of these new AI techniques (Bex et al., 2017). in a court decision makes it possible to give a However, the arrival of these new techniques has brought better understanding of the decision mechanisms a number of ethical issues. Firstly, machine learning and carried out by the judge. By using a large amount data mining techniques are capable of exploiting personal of court decisions in matters of divorce produced and legal data that are more and more easily accessible on by French jurisdictions and by looking at the variables the Internet, leading to questions about privacy preserving, that allow to allocate an alimony or not, and or even attacks on democracy (Wylie, 2019). Secondly, to define its amount, we seek to identify if there artificial intelligence programs reason in a simplistic way, may be extralegal factors in the decisions taken but the real world is complex, especially in the legal field by the judges. From this perspective, we present which leaves a certain part to the human interpretation of an explainable AI model designed in this purpose the law and characterization of the fact. A machine learning by combining a classification with random forest program has great difficulty in dealing with the unexpected and a regression model, as a complementary tool events that happen in the real world. Intelligent system to existing decision-making scales or guidelines algorithms are black boxes that are impossible to understand, created by practitioners.

In some following posts, I will explore these other methods, such as Random Forest, Support Vector Modeling, and XGboost, to see if we can improve on this customer churn model! In my previous post, we completed a pretty in-depth walk through of the exploratory data analysis process for a customer churn analysis dataset. Our data, sourced from Kaggle, is centered around customer churn, the rate at which a commercial customer will leave the commercial platform that they are currently a (paying) customer, of a telecommunications company, Telco. Now that the EDA process has been complete, and we have a pretty good sense of what our data tells us before processing, we can move on to building a Logistic Regression classification model which will allow for us to predict whether a customer is at risk to churn from Telco's platform. The complete GitHub repository with notebooks and data walkthrough can be found here.

We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian model selection priors in sparse high-dimensional linear regression. Under compatibility conditions on the design matrix, oracle inequalities are derived for the mean-field VB approximation, implying that it converges to the sparse truth at the optimal rate and gives optimal prediction of the response vector. The empirical performance of our algorithm is studied, showing that it works comparably well as other state-of-the-art Bayesian variable selection methods. We also numerically demonstrate that the widely used coordinate-ascent variational inference (CAVI) algorithm can be highly sensitive to the parameter updating order, leading to potentially poor performance. To mitigate this, we propose a novel prioritized updating scheme that uses a data-driven updating order and performs better in simulations.

Neural Linear Models (NLM) are deep models that produce predictive uncertainty by learning features from the data and then performing Bayesian linear regression over these features. Despite their popularity, few works have focused on formally evaluating the predictive uncertainties of these models. In this work, we show that traditional training procedures for NLMs can drastically underestimate uncertainty in data-scarce regions. We identify the underlying reasons for this behavior and propose a novel training procedure for capturing useful predictive uncertainties.

Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling. The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithm, then carefully choose the most appropriate data preparation techniques to transform the raw data to best meet the expectations of the algorithm. This is slow, expensive, and requires a vast amount of expertise. An alternative approach to data preparation is to apply a suite of common and commonly useful data preparation techniques to the raw data in parallel and combine the results of all of the transforms together into a single large dataset from which a model can be fit and evaluated. This is an alternative philosophy for data preparation that treats data transforms as an approach to extract salient features from raw data to expose the structure of the problem to the learning algorithms.

Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling. The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithm, then carefully choose the most appropriate data preparation techniques to transform the raw data to best meet the expectations of the algorithm. This is slow, expensive, and requires a vast amount of expertise. An alternative approach to data preparation is to apply a suite of common and commonly useful data preparation techniques to the raw data in parallel and combine the results of all of the transforms together into a single large dataset from which a model can be fit and evaluated. This is an alternative philosophy for data preparation that treats data transforms as an approach to extract salient features from raw data to expose the structure of the problem to the learning algorithms.

Data augmentation is a powerful technique to improve performance in applications such as image and text classification tasks. Yet, there is little rigorous understanding of why and how various augmentations work. In this work, we consider a family of linear transformations and study their effects on the ridge estimator in an over-parametrized linear regression setting. First, we show that transformations which preserve the labels of the data can improve estimation by enlarging the span of the training data. Second, we show that transformations which mix data can improve estimation by playing a regularization effect. Finally, we validate our theoretical insights on MNIST. Based on the insights, we propose an augmentation scheme that searches over the space of transformations by how uncertain the model is about the transformed data. We validate our proposed scheme on image and text datasets. For example, our method outperforms RandAugment by 1.24% on CIFAR-100 using Wide-ResNet-28-10. Furthermore, we achieve comparable accuracy to the SoTA Adversarial AutoAugment on CIFAR datasets.

Federated learning enables the development of a machine learning model among collaborating agents without requiring them to share their underlying data. However, malicious agents who train on random data, or worse, on datasets with the result classes inverted, can weaken the combined model. BlockFLow is an accountable federated learning system that is fully decentralized and privacy-preserving. Its primary goal is to reward agents proportional to the quality of their contribution while protecting the privacy of the underlying datasets and being resilient to malicious adversaries. Specifically, BlockFLow incorporates differential privacy, introduces a novel auditing mechanism for model contribution, and uses Ethereum smart contracts to incentivize good behavior. Unlike existing auditing and accountability methods for federated learning systems, our system does not require a centralized test dataset, sharing of datasets between the agents, or one or more trusted auditors; it is fully decentralized and resilient up to a 50% collusion attack in a malicious trust model. When run on the public Ethereum blockchain, BlockFLow uses the results from the audit to reward parties with cryptocurrency based on the quality of their contribution. We evaluated BlockFLow on two datasets that offer classification tasks solvable via logistic regression models. Our results show that the resultant auditing scores reflect the quality of the honest agents' datasets. Moreover, the scores from dishonest agents are statistically lower than those from the honest agents. These results, along with the reasonable blockchain costs, demonstrate the effectiveness of BlockFLow as an accountable federated learning system.

Failure of mission-critical equipment interrupts production and results in monetary loss. The risk of unplanned equipment downtime can be minimized through Predictive Maintenance of revenue generating assets to ensure optimal performance and safe operation of equipment. However, the increased sensorization of the equipment generates a data deluge, and existing machine-learning based predictive model alone becomes inadequate for timely equipment condition predictions. In this paper, a model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equipment-based sensor network context. Within each equipment, a sensor device aggregates raw sensor data, and the equipment health status is analyzed for anomalous events. Unlike traditional black-box regression models, the proposed algorithm self-learns an optimal maintenance policy and provides actionable recommendation for each equipment. Our experimental results demonstrate the potential for broader range of equipment maintenance applications as an automatic learning framework.

Time-Series involves temporal datasets that change over a period of time and time-based attributes are of paramount importance in these datasets. The trading prices of stocks change constantly over time, and reflect various unmeasured factors such as market confidence, external influences, and other driving forces that may be hard to identify or measure. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it. Forecasting the future value of a given stock is a crucial task as investing in stock market involves higher risk.. Here, given the historical daily close price for Dow-Jones Index, we would like to prepare and compare forecasting models. The black swan theory, which predicts that anomalous events, such as a stock market crash, are much more likely to occur than would be predicted by the normal distribution.