We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory. Using Gaussian-type orbital functions, we create surrogate electronic densities of the molecule from which we compute invariant "solid harmonic scattering coefficients" that account for different types of interactions at different scales. Multi-linear regressions of various physical properties of molecules are computed from these invariant coefficients. Numerical experiments show that these regressions have near state of the art performance, even with relatively few training examples. Predictions over small sets of scattering coefficients can reach a DFT precision while being interpretable.

Scikit-learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms. This course begins by taking you through videos on linear models; with scikit-learn, you will take a machine learning approach to linear regression. As you progress, you will explore logistic regression. Then you will build models with distance metrics, including clustering. You will also look at cross-validation and post-model workflows, where you will see how to select a model that predicts well.

Abstract--This short paper describes our solution to the 2018 IEEE World Congress on Computational Intelligence One-Minute Gradual-Emotional Behavior Challenge, whose goal was to estimate continuous arousal and valence values from short videos. We designed four base regression models using visual and audio features, and then used a spectral approach to fuse them to obtain improved performance. (IEEE WCCI 2018). The dataset was composed of 420 relatively long emotion videos with an average length of 1 minute, collected from a variety of Youtube channels. Videos were separated into clips based on utterances, and each utterance's valence and arousal levels were annotated by at least five independent subjects using the Amazon Mechanical Turk tool.

The data are usually recorded in rows and columns. A column represents a variable,whereas a row represents an observation, which is a set of p 1 values for a single subject i.e. one value for the response variable and one value for each of the p predictors. Each of the variables can be classified as either quantitative or qualitative. A technique used in cases where the response variable is binary is called logistic regression. In regression analysis, the predictor variables can be either quantitative and or qualitative. For the purpose of computations, however, the qualitative variables, if any, have to be coded into a set of indicator or dummy variables.

We consider multi-response and multitask regression models, where the parameter matrix to be estimated is expected to have an unknown grouping structure. The groupings can be along tasks, or features, or both, the last one indicating a bi-cluster or "checkerboard" structure. Discovering this grouping structure along with parameter inference makes sense in several applications, such as multi-response Genome-Wide Association Studies. This additional structure can not only can be leveraged for more accurate parameter estimation, but it also provides valuable information on the underlying data mechanisms (e.g. relationships among genotypes and phenotypes in GWAS). In this paper, we propose two formulations to simultaneously learn the parameter matrix and its group structures, based on convex regularization penalties. We present optimization approaches to solve the resulting problems and provide numerical convergence guarantees. Our approaches are validated on extensive simulations and real datasets concerning phenotypes and genotypes of plant varieties.

In machine learning, there are many ways to build a product or solution and each way assumes something different. Many times, it's not obvious how to navigate and identify which assumptions are reasonable. People new to machine learning make mistakes, which in hindsight will often feel silly. I've created a list of the top mistakes that novice machine learning engineers make. Hopefully, you can learn from these common errors and create more robust solutions that bring real value.

In this paper we explore different regression models based on Clusterwise Linear Regression (CLR). CLR aims to find the partition of the data into $k$ clusters, such that linear regressions fitted to each of the clusters minimize overall mean squared error on the whole data. The main obstacle preventing to use found regression models for prediction on the unseen test points is the absence of a reasonable way to obtain CLR cluster labels when the values of target variable are unknown. In this paper we propose two novel approaches on how to solve this problem. The first approach, predictive CLR builds a separate classification model to predict test CLR labels. The second approach, constrained CLR utilizes a set of user-specified constraints that enforce certain points to go to the same clusters. Assuming the constraint values are known for the test points, they can be directly used to assign CLR labels. We evaluate these two approaches on three UCI ML datasets as well as on a large corpus of health insurance claims. We show that both of the proposed algorithms significantly improve over the known CLR-based regression methods. Moreover, predictive CLR consistently outperforms linear regression and random forest, and shows comparable performance to support vector regression on UCI ML datasets. The constrained CLR approach achieves the best performance on the health insurance dataset, while enjoying only $\approx 20$ times increased computational time over linear regression.

There are a lot of articles about how to use Python for solving Machine Learning problems, with this article I start series of materials on how to use modern C for solving same problems and which libraries can be used. I assume that readers are already familiar with Machine Learning concepts and will concentrate on programming issues only. The first part is about creating Polynomial Regression model with XTensor library. This is C library for numerical analysis with multi-dimensional array expressions, and containers of XTensor are inspired by NumPy. A lot of functions in this library also have semantic similar to NumPy.so should be easier to start with this library rather then with Eigen or ViennaCL if you already familiar with NumPy.

Moderator models are often used to examine when an independent variable influences a dependent variable. More specifically, moderators are used to identify factors that change the relationship between independent (X) and dependent (Y) variables. In this article, I explain how moderation in regression works, and then demonstrate how to do a hierarchical, moderated, multiple regression analysis in R. Hierarchical, moderated, multiple regression analysis in R can get pretty complicated so let's start at the very beginning. Y is the dependent variable whereas the variable X is independent i.e. the regression model tries to explain the causality between the two variables. The above equation has a single independent variable.

Every year, thousands of people receive consumer product related injuries. Research indicates that online customer reviews can be processed to autonomously identify product safety issues. Early identification of safety issues can lead to earlier recalls, and thus fewer injuries and deaths. A dataset of product reviews from Amazon.com was compiled, along with \emph{SaferProducts.gov} complaints and recall descriptions from the Consumer Product Safety Commission (CPSC) and European Commission Rapid Alert system. A system was built to clean the collected text and to extract relevant features. Dimensionality reduction was performed by computing feature relevance through a Random Forest and discarding features with low information gain. Various classifiers were analyzed, including Logistic Regression, SVMs, Na{\"i}ve-Bayes, Random Forests, and an Ensemble classifier. Experimentation with various features and classifier combinations resulted in a logistic regression model with 70.2\% precision in the top 50 reviews surfaced. This classifier outperforms all benchmarks set by related literature and consumer product safety professionals.