Pover-T Tests: Predicting Poverty
Goal of the Competition
The World Bank is aiming to end extreme poverty by 2030. Crucial to this goal are techniques for determining which poverty reduction strategies work and which ones do not. But measuring poverty reduction requires measuring poverty in the first place, and it turns out that measuring poverty is pretty hard. The World Bank helps developing countries measure poverty by conducting in-depth household surveys with a subset of the country's population. To measure poverty, most of these surveys collect detailed data on household consumption – everything from food and transportation habits to healthcare access and sporting events – in order to get a clearer picture of a household's poverty status.
Can you harness the power of these data to identify the strongest predictors of poverty? Right now measuring poverty is hard, time consuming, and expensive. By building better models, we can run surveys with fewer, more targeted questions that rapidly and cheaply measure the effectiveness of new policies and interventions. The more accurate our models, the more accurately we can target interventions and iterate on policies, maximizing the impact and cost-effectiveness of these strategies.
What's in this Repository
This repository contains code volunteered from leading competitors in the Pover-T Tests: Predicting Poverty DrivenData challenge.
Winning code for other DrivenData competitions is available in the competition-winners repository.
Winning Submissions
| Place | Team or User | Public Score | Private Score | Summary of Model |
|---|---|---|---|---|
| 1 | Ag100 | 0.14469 | 0.14797 | The solution is an ensemble of models built using gradient boosting (GB with lightgbm) and neural networks (NN with keras). In order to reduce the variance of the neural networks we bagged (Bootstrap aggregating) some models, sampling 8 times, with replacement, 95% of the training set and averaging the predictions across models. We tried to take into account the only interpretable feature – household size – when normalizing the features created from the data of the individual household members. The most challenging part was feature selection: removing unnecessary features while adding new features from the data of the individual household members. We did this using a couple of techniques. A successful one was to simultaneously fit a model to the core group of features and to the group of features we wanted to add/test. We then evaluated the effect that a random permutation on each individual feature had on the predictions of that model. After going through every feature, we removed the ones for which we registered a score improvement. We used 20-fold cv in Countries A and B in the hope that better out-of-fold predictions would translate into better fits of the optimal weight of each model in the ensemble. |
| 2 | sagol | 0.14593 | 0.15067 | Since there are many categorical features in the data and a small size of samples, I decided to use tree-based algorithms (CatBoost, XGBoost and LightGBM). In the final submission, I used an ensemble of the results of each algorithm. The smallest correlation was between CatBoost vs XGBoost and CatBoost vs LightGBM. So, I chose the weights 0.4 for XGBoost and CatBoost, and 0.2 for LightGBM. Since the data for countries are not balanced, this fact had to be taken into account in the calculations. Cross-validation showed that for countries A and B it is better to use weights in the algorithms, and for country B to make up-sampling. Since the features was too much, the generation of new ones had to be treated very carefully and only those that gave a significant increase in cross-validation(the number of unique categories for households, the number of residents, the number of positive and negative values). In addition, cross-validation showed that the data of their individual set for country A did not reduce the error. After the generation of the feature, I filtered using the feature_importance parameter for each of the algorithms separately, and this significantly reduced the number of features without loss of quality. |
| 3 | LastRocky | 0.15257 | 0.15132 | My method is basically a combination of gradient boosted decision tree and neural network. For gradient boosted decision tree models I used Lightgbm and Xgboost open source libraries. For neural network, I used Keras to build the model. I built one model for each of country A, country B and country C. The final submission is a weighted average of 10-folds cross validation combination of Xgboost model, Lightgbm model and Neural network model for Country A and B. For country C, 10-folds cross validation combination of Xgboost and Lightgbm is used. But after this competition I found 20-folds cross validation improve local validation score a little bit, so I submit the code, where I set it to 20 folds cross validation. |
| Bonus | avsolatorio | 0.14966 | 0.15161 | This competition is very challenging due to the very anonymous nature of the data. Only very limited feature engineering can be done. Also, without any comprehensible insights about the features, I just decided to use an L1 regularized Logistic Regression model (with its sparse solutions in mind) to set a benchmark. It ended up working pretty well with the country A dataset. I also threw in the usual models such as Random Forest, XGBoost, LightGBM, and Neural Networks. The tree based models worked pretty well in country B and C datasets - most likely due to the heavily imbalanced nature of the two datasets. At first, my plan is to perform Bayesian optimization on the parameters to come up with a single best parameter set for each. Later, I realized that I can just combine the best performing variation of the Bayesian optimized models. I ended up building 100 variations by taking the top 20 variations of each model as the meta-models to be used in the final predictions. The common method in combining these many models is stacking. However, stacking surprisingly didn't work well. It took me a while to realize that I can fairly easily combine an arbitrary number of model results by blending them. Common appoach in blending is by manually assigning weights based on intuition. While this method works for fairly small number of models, this is not scalable to quite a number of models - in this case 100. I was able to solve this problem via a constrained optimization of the model weights. In the weight optimization process, I used the out-of-fold predictions from each meta-models and constrained the weights such that they sum up to one. The coefficients of the linear combination of the out-of-fold predictions are then optimized based on the loss metric against the actual values. This process was done using stratified cross validation (10-folds) and the coefficients for each fold were then averaged to blend the test meta-predictions. I think the model could have scored higher if I used more meta-models instead of just selecting the best 20 for each base model. |