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interaction term

Interpreting the Coefficients of a Regression Model with an Interaction Term: A Detailed…


Adding an interaction term to a regression model becomes necessary when the relationship between an explanatory variable and an outcome variable depends on the value/level of another explanatory variable. Although the addition of an interaction term can result in a more meaningful empirical model, it simultaneously complicates the interpretation of model coefficients. In this article, we are going to learn how to interpret the coefficients of a regression model that includes a two-way interaction term. By the end of this article, we should understand how the interpretation of model coefficients differs between a model with an interaction term and a model without an interaction term. We are going to use the statistical software R for building the models and visualizing the outcomes.

Adaptive Explainable Neural Networks (AxNNs) Artificial Intelligence

While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the results. We develop a new framework called Adaptive Explainable Neural Networks (AxNN) for achieving the dual goals of good predictive performance and model interpretability. For predictive performance, we build a structured neural network made up of ensembles of generalized additive model networks and additive index models (through explainable neural networks) using a two-stage process. This can be done using either a boosting or a stacking ensemble. For interpretability, we show how to decompose the results of AxNN into main effects and higher-order interaction effects. The computations are inherited from Google's open source tool AdaNet and can be efficiently accelerated by training with distributed computing. The results are illustrated on simulated and real datasets.

A gentle introduction to GA2Ms, a white box model


One final possibility: regulations dictate that you need to fully describe your model. In that case, it could be useful to have human-readable internals for reference.

An Automatic Interaction Detection Hybrid Model for Bankcard Response Classification Machine Learning

In this paper, we propose a hybrid bankcard response model, which integrates decision tree based chi-square automatic interaction detection (CHAID) into logistic regression. In the first stage of the hybrid model, CHAID analysis is used to detect the possibly potential variable interactions. Then in the second stage, these potential interactions are served as the additional input variables in logistic regression. The motivation of the proposed hybrid model is that adding variable interactions may improve the performance of logistic regression. To demonstrate the effectiveness of the proposed hybrid model, it is evaluated on a real credit customer response data set. As the results reveal, by identifying potential interactions among independent variables, the proposed hybrid approach outperforms the logistic regression without searching for interactions in terms of classification accuracy, the area under the receiver operating characteristic curve (ROC), and Kolmogorov-Smirnov (KS) statistics. Furthermore, CHAID analysis for interaction detection is much more computationally efficient than the stepwise search mentioned above and some identified interactions are shown to have statistically significant predictive power on the target variable. Last but not least, the customer profile created based on the CHAID tree provides a reasonable interpretation of the interactions, which is the required by regulations of the credit industry. Hence, this study provides an alternative for handling bankcard classification tasks.

IMMIGRATE: A Margin-based Feature Selection Method with Interaction Terms Machine Learning

By balancing margin-quantity maximization and margin-quality maximization, the proposed IMMIGRATE algorithm considers both local and global information when using margin-based frameworks. We here derive a new mathematical interpretation of margin-based cost function by using the quadratic form distance (QFD) and applying both the large-margin and max-min entropy principles. We also design a new principle for classifying new samples and propose a Bayesian framework to iteratively minimize the cost function. We demonstrate the power of our new method by comparing it with 16 widely used classifiers (e.g. Support Vector Machine, k-nearest neighbors, RELIEF, etc.) including some classifiers that are capable of identifying interaction terms (e.g. SODA, hierNet, etc.) on synthetic dataset, five gene expression datasets, and twenty UCI machine learning datasets. Our method is able to outperform other methods in most cases.

Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks Machine Learning

Motivated by the pursuit of a systematic computational and algorithmic understanding of Generative Adversarial Networks (GANs), we present a simple yet unified non-asymptotic local convergence theory for smooth two-player games, which subsumes several discrete-time gradient-based saddle point dynamics. The analysis reveals the surprising nature of the off-diagonal interaction term as both a blessing and a curse. On the one hand, this interaction term explains the origin of the slow-down effect in the convergence of Simultaneous Gradient Ascent (SGA) to stable Nash equilibria. On the other hand, for the unstable equilibria, exponential convergence can be proved thanks to the interaction term, for three modified dynamics which have been proposed to stabilize GAN training: Optimistic Mirror Descent (OMD), Consensus Optimization (CO) and Predictive Method (PM). The analysis uncovers the intimate connections among these stabilizing techniques, and provides detailed characterization on the choice of learning rate.

Data Science Simplified Part 9: Interactions and Limitations of Regression Models


The model predicts or estimates price (target) as a function of engine size, horse power, and width (predictors). Recall that multivariate regression model assumes independence between the independent predictors. It treats horsepower, engine size, and width as if they are not related. In practice, variables are rarely independent. This blog post will address this question.

Data Science Simplified Part 9: Interactions and Limitations of Regression Models


In the last few blog posts of this series discussed regression models at length. Fernando has built a multivariate regression model. What if there are relations between horsepower, engine size and width? Can these relationships be modeled? This blog post will address this question.

Making data science accessible – Logistic Regression


Regression is a modelling technique for predicting the values of an outcome variable from one or more explanatory variables. Logistic Regression is a specific approach for describing a binary outcome variable (for example yes/no). Let's assume you are own a new boutique shop. You have a list of potential clients you are thinking of inviting to a special event with the aim of maximizing the number of sales – who should you invite? Data on previous events you have run is a great starting point here, allowing you to predict an individual's likelihood of buying given the information you have on them.

Algorithms for Learning Sparse Additive Models with Interactions in High Dimensions Machine Learning

A function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is a Sparse Additive Model (SPAM), if it is of the form $f(\mathbf{x}) = \sum_{l \in \mathcal{S}}\phi_{l}(x_l)$ where $\mathcal{S} \subset [d]$, $|\mathcal{S}| \ll d$. Assuming $\phi$'s, $\mathcal{S}$ to be unknown, there exists extensive work for estimating $f$ from its samples. In this work, we consider a generalized version of SPAMs, that also allows for the presence of a sparse number of second order interaction terms. For some $\mathcal{S}_1 \subset [d], \mathcal{S}_2 \subset {[d] \choose 2}$, with $|\mathcal{S}_1| \ll d, |\mathcal{S}_2| \ll d^2$, the function $f$ is now assumed to be of the form: $\sum_{p \in \mathcal{S}_1}\phi_{p} (x_p) + \sum_{(l,l^{\prime}) \in \mathcal{S}_2}\phi_{(l,l^{\prime})} (x_l,x_{l^{\prime}})$. Assuming we have the freedom to query $f$ anywhere in its domain, we derive efficient algorithms that provably recover $\mathcal{S}_1,\mathcal{S}_2$ with finite sample bounds. Our analysis covers the noiseless setting where exact samples of $f$ are obtained, and also extends to the noisy setting where the queries are corrupted with noise. For the noisy setting in particular, we consider two noise models namely: i.i.d Gaussian noise and arbitrary but bounded noise. Our main methods for identification of $\mathcal{S}_2$ essentially rely on estimation of sparse Hessian matrices, for which we provide two novel compressed sensing based schemes. Once $\mathcal{S}_1, \mathcal{S}_2$ are known, we show how the individual components $\phi_p$, $\phi_{(l,l^{\prime})}$ can be estimated via additional queries of $f$, with uniform error bounds. Lastly, we provide simulation results on synthetic data that validate our theoretical findings.