This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict tactical solutions to a given operational problem. In this context, the tactical solution is less detailed than the operational one but it has to be computed in very short time and under imperfect information. The problem is of importance in various applications where tactical and operational planning problems are interrelated and information about the operational problem is revealed over time. This is for instance the case in certain capacity planning and demand management systems. We formulate the problem as a two-stage optimal prediction stochastic program whose solution we predict with a supervised machine learning algorithm. The training data set consists of a large number of deterministic (second stage) problems generated by controlled probabilistic sampling. The labels are computed based on solutions to the deterministic problems (solved independently and offline) employing appropriate aggregation and subselection methods to address uncertainty. Results on our motivating application in load planning for rail transportation show that deep learning algorithms produce highly accurate predictions in very short computing time (milliseconds or less). The prediction accuracy is comparable to solutions computed by sample average approximation of the stochastic program.

This paper describes the autofeat Python library, which provides a scikit-learn style linear regression model with automatic feature engineering and selection capabilities. Complex non-linear machine learning models such as neural networks are in practice often difficult to train and even harder to explain to non-statisticians, who require transparent analysis results as a basis for important business decisions. While linear models are efficient and intuitive, they generally provide lower prediction accuracies. Our library provides a multi-step feature engineering and selection process, where first a large pool of non-linear features is generated, from which then a small and robust set of meaningful features is selected, which improve the prediction accuracy of a linear model while retaining its interpretability.

Birg{\'e} and Massart proposed in 2001 the slope heuristics as a way to choose optimally from data an unknown multiplicative constant in front of a penalty. It is built upon the notion of minimal penalty, and it has been generalized since to some 'minimal-penalty algorithms'. This paper reviews the theoretical results obtained for such algorithms, with a self-contained proof in the simplest framework, precise proof ideas for further generalizations, and a few new results. Explicit connections are made with residual-variance estimators-with an original contribution on this topic, showing that for this task the slope heuristics performs almost as well as a residual-based estimator with the best model choice-and some classical algorithms such as L-curve or elbow heuristics, Mallows' C p , and Akaike's FPE. Practical issues are also addressed, including two new practical definitions of minimal-penalty algorithms that are compared on synthetic data to previously-proposed definitions. Finally, several conjectures and open problems are suggested as future research directions.

In an earlier post, I discussed a model agnostic feature selection technique called forward feature selection which basically extracted the most important features required for the optimal value of chosen KPI. It had one caveat though -- large time complexity. In order to circumvent that issue feature importance can directly be obtained from the model being trained. In this post, I will consider 2 classification and 1 regression algorithms to explain model-based feature importance in detail. An inherently binary classification algorithm, it tries to find the best hyperplane in k-dimensional space that separates the 2 classes, minimizing logistic loss.

Python Type Conversion, Decisions and Loops – Type Conversion – Decision making -- a) If.. -- b) If..else -- c) Nested if..else -- d) If..elif..else – Loops -- a) For loop -- b) While loop -- c) Nested loops -- d) break, pass and continue

Let's say I am given an Excel sheet with data about various fruits and I have to tell which look like Apples. What I will do is ask a question "Which fruits are red and round?" and divide all fruits which answer yes and no to the question. Now, All Red and Round fruits might not be apples and all apples won't be red and round. So I will ask a question "Which fruits have red or yellow color hints on them? " on red and round fruits and will ask "Which fruits are green and round?" on not red and round fruits. Based on these questions I can tell with considerable accuracy which are apples. This cascade of questions is what a decision tree is. However, this is a decision tree based on my intuition.

Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC). With the development of Artificial Intelligence and computer technologies, reinforcement learning (RL) can be implemented in multiple realistic scenarios and help people to solve thousands of real-world problems. Reinforcement Learning, which is considered as the art of future AI, builds the bridge between agents and environments through Markov Decision Chain or Neural Network and has seldom been used in power system. The art of RL is that once the simulator for a specific environment is built, the algorithm can keep learning from the environment. Therefore, RL is capable of dealing with constantly changing simulator inputs such as power demand, the condition of power system and outdoor temperature, etc. Compared with the existing distribution power system planning mechanisms and the related game theoretical methodologies, our proposed algorithm can plan and optimize the hourly energy usage, and have the ability to corporate with even shorter time window if needed.

I'm happy to announce that version 0.8.0 of my ggeffects-package is on CRAN now. The update has fixed some bugs from the previous version and comes along with many new features or improvements. One major part that was addressed in the latest version are fixed and improvements for mixed models, especially zero-inflated mixed models (fitted with the glmmTMB-package). In this post, I want to demonstrate the different options to calculate and visualize marginal effects from mixed models. Basically, the type of predictions, i.e. whether to account for the uncertainty of random effects or not, can be set with the type-argument. The default, type "fe", means that predictions are on the population-level and do not account for the random effect variances.

We study the problem of offline learning in automated decision systems under the contextual bandits model. We are given logged historical data consisting of contexts, (randomized) actions, and (nonnegative) rewards. A common goal is to evaluate what would happen if different actions were taken in the same contexts, so as to optimize the action policies accordingly. The typical approach to this problem, inverse probability weighted estimation (IPWE) [Bottou et al., 2013], requires logged action probabilities, which may be missing in practice due to engineering complications. Even when available, small action probabilities cause large uncertainty in IPWE, rendering the corresponding results insignificant. To solve both problems, we show how one can use policy improvement (PIL) objectives, regularized by policy imitation (IML). We motivate and analyze PIL as an extension to Clipped-IPWE, by showing that both are lower-bound surrogates to the vanilla IPWE. We also formally connect IML to IPWE variance estimation [Swaminathan and Joachims 2015] and natural policy gradients. Without probability logging, our PIL-IML interpretations justify and improve, by reward-weighting, the state-of-art cross-entropy (CE) loss that predicts the action items among all action candidates available in the same contexts. With probability logging, our main theoretical contribution connects IML-underfitting to the existence of either confounding variables or model misspecification. We show the value and accuracy of our insights by simulations based on Simpson's paradox, standard UCI multiclass-to-bandit conversions and on the Criteo counterfactual analysis challenge dataset.

Objective: Predict individual septic children's personalized physiologic responses to vasoactive titrations by training a Recurrent Neural Network (RNN) using EMR data. Materials and Methods: This study retrospectively analyzed EMR of patients admitted to a pediatric ICU from 2009 to 2017. Data included charted time series vitals, labs, drugs, and interventions of children with septic shock treated with dopamine, epinephrine, or norepinephrine. A RNN was trained to predict responses in heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP) to 8,640 titrations during 652 septic episodes and evaluated on a holdout set of 3,883 titrations during 254 episodes. A linear regression model using titration data as its sole input was also developed and compared to the RNN model. Evaluation methods included the correlation coefficient between actual physiologic responses and RNN predictions, mean absolute error (MAE), and area under the receiver operating characteristic curve (AUC). Results: The actual physiologic responses displayed significant variability and were more accurately predicted by the RNN model than by titration alone (r=0.20 vs r=0.05, p<0.01). The RNN showed MAE and AUC improvements over the linear model. The RNN's MAEs associated with dopamine and epinephrine were 1-3% lower than the linear regression model MAE for HR, SBP, DBP, and MAP. Across all vitals vasoactives, the RNN achieved 1-19% AUC improvement over the linear model. Conclusion: This initial attempt in pediatric critical care to predict individual physiologic responses to vasoactive dose changes in children with septic shock demonstrated an RNN model showed some improvement over a linear model. While not yet clinically applicable, further development may assist clinical administration of vasoactive medications in children with septic shock.