We examined the use of the Ising model as an $L_0$ regularization method for field-aware factorization machines (FFM). This approach improves generalization performance and has the advantage of simultaneously determining the best feature combinations for each of several groups. We can deepen the interpretation and understanding of the model from the similarities and differences in the features selected in each group.

Introduction: The amount of data generated by original research is growing exponentially. Publicly releasing them is recommended to comply with the Open Science principles. However, data collected from human participants cannot be released as-is without raising privacy concerns. Fully synthetic data represent a promising answer to this challenge. This approach is explored by the French Centre de Recherche en {\'E}pid{\'e}miologie et Sant{\'e} des Populations in the form of a synthetic data generation framework based on Classification and Regression Trees and an original distance-based filtering. The goal of this work was to develop a refined version of this framework and to assess its risk-utility profile with empirical and formal tools, including novel ones developed for the purpose of this evaluation.Materials and Methods: Our synthesis framework consists of four successive steps, each of which is designed to prevent specific risks of disclosure. We assessed its performance by applying two or more of these steps to a rich epidemiological dataset. Privacy and utility metrics were computed for each of the resulting synthetic datasets, which were further assessed using machine learning approaches.Results: Computed metrics showed a satisfactory level of protection against attribute disclosure attacks for each synthetic dataset, especially when the full framework was used. Membership disclosure attacks were formally prevented without significantly altering the data. Machine learning approaches showed a low risk of success for simulated singling out and linkability attacks. Distributional and inferential similarity with the original data were high with all datasets.Discussion: This work showed the technical feasibility of generating publicly releasable synthetic data using a multi-step framework. Formal and empirical tools specifically developed for this demonstration are a valuable contribution to this field. Further research should focus on the extension and validation of these tools, in an effort to specify the intrinsic qualities of alternative data synthesis methods.Conclusion: By successfully assessing the quality of data produced using a novel multi-step synthetic data generation framework, we showed the technical and conceptual soundness of the Open-CESP initiative, which seems ripe for full-scale implementation.

For the purpose of this project, I used 2 supervised machine learning methods. I compared several classification and regression models on how well they are able to identify future donors and estimate how much they will donate. These models were used to predict the outcome of the next campaign. Yemego NGO is a fictitious charity organization that provides programs and services for veterans with spinal cord injuries or diseases. Direct mailing campaigns are used to raise funds for charity organizations.

Correlation plots are the best way to show the pattern and relationship. If you have utilized some other correlation plot please mention in the comment box will include the same. The post Correlation Analysis Different Types of Plots in R appeared first on finnstats.

Clustering can be used to extract insights from data or to verify some of the assumptions held by the domain experts, namely data segmentation. In the literature, few methods can be applied in clustering qualitative values using the context associated with other variables present in the data, without losing interpretability. Moreover, the metrics for calculating dissimilarity between qualitative values often scale poorly for high dimensional mixed datasets. In this study, we propose a novel method for clustering qualitative values, based on Hierarchical Clustering (HQC), and using Maximum Mean Discrepancy. HQC maintains the original interpretability of the qualitative information present in the dataset. We apply HQC to two datasets. Using a mixed dataset provided by Spotify, we showcase how our method can be used for clustering music artists based on the quantitative features of thousands of songs. In addition, using financial features of companies, we cluster company industries, and discuss the implications in investment portfolios diversification.

A couple of days ago a friend of mine, who started exploring deep learning, asked me "Hey man, can you explain this Cross-entropy thing to me?". Now, that is a tough question, because this topic is never set with me right. My self-doubt kicked in, so in my mind, this question actually sounded more like "Can you explain Cross-entropy to yourself?". Even in my conference talks, I usually avoid mentioning it. Every time I start talking about it, I get all confused and convoluted.

This post could also be called "everything I learned during the first year of my PhD while working with a massive electronic health record data set." Here, I'll overview the difference between categorical and quantitative variables, and I'll explain how to prepare tabular data for a neural network model, including: Best of all, broadly-applicable code is provided to do all of the above on your tabular data! A categorical variable is a variable that takes on different category names. Quantitative variables have numerical values that usually represent the result of a measurement. If your variable is the NAME of a laboratory test, then that's a nominal (categorical) variable because the values it can take on are different names of different lab tests, e.g.

Computer simulations often involve both qualitative and numerical inputs. Existing Gaussian process (GP) methods for handling this mainly assume a different response surface for each combination of levels of the qualitative factors and relate them via a multiresponse cross-covariance matrix. We introduce a substantially different approach that maps each qualitative factor to an underlying numerical latent variable (LV), with the mapped value for each level estimated similarly to the covariance lengthscale parameters. This provides a parsimonious GP parameterization that treats qualitative factors the same as numerical variables and views them as effecting the response via similar physical mechanisms. This has strong physical justification, as the effects of a qualitative factor in any physics-based simulation model must always be due to some underlying numerical variables. Even when the underlying variables are many, sufficient dimension reduction arguments imply that their effects can be represented by a low-dimensional LV. This conjecture is supported by the superior predictive performance observed across a variety of examples. Moreover, the mapped LVs provide substantial insight into the nature and effects of the qualitative factors.

For about thirty years, data analysis methods have largely demonstrated their effectiveness in the processing of data in many fields. Data reduction is one of these methods and part of the descriptive (or exploratory) statistics. It tries to summarize a sample of data using graphs or numerical characteristics. The main interpretation of data reduction is reducing the number of dimensions. This implies that data reduction is part of the multivariate exploratory statistics which seek to reduce the number of data dimensions by extracting a number of factors, dimensions, clusters, etc., which explain the dispersion of (multidimensional) data.

We propose a method that performs anomaly detection and localisation within heterogeneous data using a pairwise undirected mixed graphical model. The data are a mixture of categorical and quantitative variables, and the model is learned over a dataset that is supposed not to contain any anomaly. We then use the model over temporal data, potentially a data stream, using a version of the two-sided CUSUM algorithm. The proposed decision statistic is based on a conditional likelihood ratio computed for each variable given the others. Our results show that this function allows to detect anomalies variable by variable, and thus to localise the variables involved in the anomalies more precisely than univariate methods based on simple marginals.