Food security is more prominent on the policy agenda today than it has been in the past, thanks to recent food shortages at both the regional and global levels as well as renewed promises from major donor countries to combat chronic hunger. One field where machine learning can be used is in the classification of household food insecurity. In this study, we establish a robust methodology to categorize whether or not a household is being food secure and food insecure by machine learning algorithms. In this study, we have used ten machine learning algorithms to classify the food security status of the Household. Gradient Boosting (GB), Random Forest (RF), Extra Tree (ET), Bagging, K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Ada Boost (AB) and Naive Bayes were the classification algorithms used throughout this study (NB). Then, we perform classification tasks from developing data set for household food security status by gathering data from HICE survey data and validating it by Domain Experts. The performance of all classifiers has better results for all performance metrics. The performance of the Random Forest and Gradient Boosting models are outstanding with a testing accuracy of 0.9997 and the other classifier such as Bagging, Decision tree, Ada Boost, Extra tree, K-nearest neighbor, Logistic Regression, SVM and Naive Bayes are scored 0.9996, 0.09996, 0.9994, 0.95675, 0.9415, 0.8915, 0.7853 and 0.7595, respectively.

Amazon Redshift ML allows data analysts, developers, and data scientists to train machine learning (ML) models using SQL. In previous posts, we demonstrated how customers can use the automatic model training capability of Amazon Redshift to train their classification and regression models. Redshift ML provides several capabilities for data scientists. It allows you to create a model using SQL and specify your algorithm as XGBoost. It also lets you bring your pre-trained XGBoost model into Amazon Redshift for local inference.

Welcome to this hands-on, guided introduction to Explainable Machine Learning with LIME and H2O in R. By the end of this project, you will be able to use the LIME and H2O packages in R for automatic and interpretable machine learning, build classification models quickly with H2O AutoML and explain and interpret model predictions using LIME. Machine learning (ML) models such as Random Forests, Gradient Boosted Machines, Neural Networks, Stacked Ensembles, etc., are often considered black boxes. However, they are more accurate for predicting non-linear phenomena due to their flexibility. Experts agree that higher accuracy often comes at the price of interpretability, which is critical to business adoption, trust, regulatory oversight (e.g., GDPR, Right to Explanation, etc.). As more industries from healthcare to banking are adopting ML models, their predictions are being used to justify the cost of healthcare and for loan approvals or denials.

The intuition behind the random forest algorithm can be split into two big parts: the random part and the forest part. Let us start with the latter. A forest in real life is made up of a bunch of trees. A random forest classifier is made up of a bunch of decision tree classifiers (here and throughout the text -- DT). The exact amount of DTs that make up the whole forest is defined with the n_estimators variable mentioned earlier.

Like many predictive models, random forests provide a point prediction for a new observation. Besides the point prediction, it is important to quantify the uncertainty in the prediction. Prediction intervals provide information about the reliability of the point predictions. We have developed a comprehensive R package, RFpredInterval, that integrates 16 methods to build prediction intervals with random forests and boosted forests. The methods implemented in the package are a new method to build prediction intervals with boosted forests (PIBF) and 15 different variants to produce prediction intervals with random forests proposed by Roy and Larocque (2020). We perform an extensive simulation study and apply real data analyses to compare the performance of the proposed method to ten existing methods to build prediction intervals with random forests. The results show that the proposed method is very competitive and, globally, it outperforms the competing methods.

Climate models are critical tools for developing strategies to manage the risks posed by sea-level rise to coastal communities. While these models are necessary for understanding climate risks, there is a level of uncertainty inherent in each parameter in the models. This model parametric uncertainty leads to uncertainty in future climate risks. Consequently, there is a need to understand how those parameter uncertainties impact our assessment of future climate risks and the efficacy of strategies to manage them. Here, we use random forests to examine the parametric drivers of future climate risk and how the relative importances of those drivers change over time. We find that the equilibrium climate sensitivity and a factor that scales the effect of aerosols on radiative forcing are consistently the most important climate model parametric uncertainties throughout the 2020 to 2150 interval for both low and high radiative forcing scenarios. The near-term hazards of high-end sea-level rise are driven primarily by thermal expansion, while the longer-term hazards are associated with mass loss from the Antarctic and Greenland ice sheets. Our results highlight the practical importance of considering time-evolving parametric uncertainties when developing strategies to manage future climate risks.

In this paper, we propose a density estimation algorithm called \textit{Gradient Boosting Histogram Transform} (GBHT), where we adopt the \textit{Negative Log Likelihood} as the loss function to make the boosting procedure available for the unsupervised tasks. From a learning theory viewpoint, we first prove fast convergence rates for GBHT with the smoothness assumption that the underlying density function lies in the space $C^{0,\alpha}$. Then when the target density function lies in spaces $C^{1,\alpha}$, we present an upper bound for GBHT which is smaller than the lower bound of its corresponding base learner, in the sense of convergence rates. To the best of our knowledge, we make the first attempt to theoretically explain why boosting can enhance the performance of its base learners for density estimation problems. In experiments, we not only conduct performance comparisons with the widely used KDE, but also apply GBHT to anomaly detection to showcase a further application of GBHT.

If you've worked as a data scientist, competed in Kaggle competitions, or even browsed data science articles on the internet, there's a high chance that you've heard of XGBoost. Even today, it is often the go-to algorithm for many Kagglers and data scientists working on general machine learning tasks. While XGBoost is popular for good reasons, it does have some limitations, which I mentioned in my article below. Odds are, you've probably heard of XGBoost, have you ever heard of CatBoost? CatBoost is another open-source gradient boosting library that was created by researchers at Yandex.

Gradient Boosting models such as XGBoost, LightGBM and Catboost have long been considered best in class for tabular data. Even with rapid progress in NLP and Computer Vision, Neural Networks are still routinely surpassed by tree-based models on tabular data. Enter Google's TabNet in 2019. According to the paper, this Neural Network was able to outperform the leading tree based models across a variety of benchmarks. Not only that, it is considerably more explainable than boosted tree models as it has built-in explainability.

Many single-target regression problems require estimates of uncertainty along with the point predictions. Probabilistic regression algorithms are well-suited for these tasks. However, the options are much more limited when the prediction target is multivariate and a joint measure of uncertainty is required. For example, in predicting a 2D velocity vector a joint uncertainty would quantify the probability of any vector in the plane, which would be more expressive than two separate uncertainties on the x- and y- components. To enable joint probabilistic regression, we propose a Natural Gradient Boosting (NGBoost) approach based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution. Our method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches. We demonstrate these claims in simulation and with a case study predicting two-dimensional oceanographic velocity data. An implementation of our method is available at https://github.com/stanfordmlgroup/ngboost.