R Parallel Processing for Developing Ensemble Learning with SuperLearner

Ensemble learning which included multiple learners, i.e. Machine Learning algorithms, may take much longer time than expected to develop. When using a search grid for parameter optimization to train an ensemble, depending on the included algorithms, the number of variables, and the corresponding iterations based on combinations of parameter settings and with cross validation, it may take a while to produce results, assuming not running out of computing resources.

For me, parallel processing becomes essential for working on a Machine Learning project. Speaking from my experience, while developing ensemble learning with SuperLearner, in one scenario, there were total 112 learners generated from a test grid. And the wait time was just too long to maintain productivity. Later I ran parallel processing in a single host to speed up the process. I used 3 cpus of my i7-16 GB RAM laptop for small test runs and 15 vcpus of an Azure D16 Series VM with 64 RAM, as shown above, for training stable models with large amount of data. Notice that despite multiple SuperLearner sessions can run concurrently in a host with multiple cpus, within SuperLearner the process remains sequential (Ref: page 7,  ‘parallel’, SuperLearner document dated Aug. 11, 2018). So using multiple cpus should and did overall reduce the elapsed time linearly (i.e. 3 cpus to cut the elapsed time to 1/3) based on my experience.

The following is one sample configuration for running parallel processing in R in Windows environment, which I employed SuperLearner for training an ensemble of ranger and xgboost. Prior to this point, I had already

  • prepared and partitioned the data for training and testing (x.train, y.train, x.test, and y.test) where y is the label,
  • configured the test grids (ranger.custom and xgboosst.custom) with function names resolved by SuperLearner

Upon finishing, the code also saved the run-time image as RDS object for later subsequent tasks to read in the image and eventually make predictions. Since SuperLearner does not have a built-in function to report the time for cross validation, I wrapped the cross validation part with system.time.

With the additional operation details in preparing and training an ensemble, this code is not a plug-and-play sample. If you are new to SuperLearner, I highly recommend first reviewing the package, parallel, and taking time to practice and experiment. On the other hand, if you have already had an ensemble model developed with SuperLearner, this sample code may be a template for converting existing training/model-fitting from sequential execution into a configuration for parallel processing. And stay tuned for my upcoming post, Part 2 of Predicting Hospital Readmissions with Ensemble Learning, with additional details on developing ensemble learning.

if (!require('parallel')) install.packages('parallel'); library(parallel)

# Create a cluster using most CPUs
cl <- makeCluster(detectCores()-1)

# Export all references to cluster nodes
clusterExport(cl, c( listWrappers()
  ,'SuperLearner' ,'CV.SuperLearner' ,'predict.SuperLearner'
   ,'nfold','x.train' ,'y.train' ,'x.test' ,'y.test' ,'family','nnls'
  ,'SL.algorithm' ,ranger.custom$names ,xgboost.custom$names

# Set a common seed for the cluster
clusterSetRNGStream(cl, iseed=135)

# Load libraries on workers
clusterEvalQ(cl, { 

# Run training session in parallel
clusterEvalQ(cl, {
  ensem.nnls <- SuperLearner(Y=y.train ,X=x.train
     ,family=family ,method=nnls ,SL.library=SL.algorithm
     );saveRDS(ensem.nnls ,'ensem.nnls')

# Do cross validation in parallel
   ensem.nnls.cv <- CV.SuperLearner(Y=y.test ,X=x.test
     ,cvControl=list(V=nfold) ,parallel=cl
     ,family=family ,method=nnls ,SL.library=SL.algorithm
     );saveRDS(ensem.nnls.cv ,'ensem.nnls.cv')



A Shiny App for Monitoring Real-Time Data Stream

This is an app developed with Shiny and R for visualizing real-time data stream. In this recording, the app ran locally, while I downloaded raw data collected from a set of Raspberry Sensor Hat devices stored in Azure cloud storage to a local disk to mimic a data pipeline for acquiring data with a batch processing.

In production, the app may run as a service locally or in cloud and a data pipeline configuration can be part of Cortana Intelligence Suite to automatically move data from cloud to local storage and vice versa for implementing  an end-to-end Machine Learning and IoT solutions.

Predicting House Price with Multiple Linear Regression

House Price Prediction

This project was to develop a Machine Learning model for predicting a house price. Despite there were a number of tree-based algorithms relevant to this application, the project was to examine linear regression and focused on specifically four models: Linear Regression, Ridge Regression, Lasso Regression and Elastic Net.



In this article, “variable”” as a general programming term and “feature” denoting a predictor employed in a Machine Learning model are used interchangeably. The following outlines my approach and highlights the logical steps which I followed for developing a Machine Learning models. The development process was highly iterative and the presented steps were not necessarily the exact order. Nevertheless, these steps correctly depict the thought process and overall strategies for developing a Machine Learning model.

  • Data Set The data set was downloaded from Kaggle House Prices: Advanced Regression Techniques. There were two files: train.csv with 1460 observations and 81 variables, while test.csv with 1459 observations and and 80 variables.
  • Missingness There were a few variables with considerable amount of missing values, essentially unusable and removed from subsequent process. Those missing at random were later imputed with values.
  • Character variables Factor variables were read in as character ones. Some character variables with several unique values. They were converted into ordinal and minimized into two or three levels for later imputing missing values and selecting features programmatically.
  • Numeric Variables There were some extreme values among numeric variables in the train data set due to the way the values were captures. For instance, those measures such as deck, porch or pool ranges from 0 when not applicable to hundreds in squared footage. When modeling these variables as predictors, those with large values might overwhelm and skew the model. These variables were minimized to just a few levels and converted to numbers better reflect real-world scenarios. Above all, the strategies to select what and determine how to convert a variable have much to do with the composition and distribution of the data. Often, the values of a variable are not as significant as the variance of those values.
  • Extreme Values and Outliers Not all variables with values larger than 1.5 IQR were removed from the train data set. Some of these extreme values appeared characteristic and influential to some model configurations. In a few test runs, removing outliers or those observations resulting residuals with much leverage actually decreased Rsquared values. For a data set, like the Kaggle House Price, the interactions among variables can be intricate since there are many variables. Making one change at a time, documenting the changes well, and backing up the settings often are the lessons I have learned well from handling outliers and extreme values of this project.
  • Imputation of Missing Values Used the package, Multivariate Imputation by Chained Equations (mice), for imputing values programmatically throughout the development.
  • Feature Selection Used the package, Boruta: Wrapper Algorithm for All Relevant Feature Selection, to initially selecting features. Subsequently, removed insignificant features from the model based on the significance level of test runs. This process was iterative and carried out along with model development. As test statistics confirming the impact or importance of a feature, it was restored or removed accordingly. An example of running Boruta is available.
  • Near Zero-Variance Variables A variable with little variance behaves like and is essential a constant with values distributed near its mean. A constant-like or near zero-variance variable contributes little to a Machine Learning model since little correlation with an outcome, namely a prediction, of applying changes to the model. With the package, Classification and Regression Training (caret), once can identify and process a near zero-variance variable programmatically.
  • Partitioning Data Partitioned the train data set into 70/30 where 70% for training and 30% for evaluating the model.
  • Cross-Validation Used 10-fold cross-validation in all training and with 5 repetitions.
  • Linear Model Overall, simply including all variables in a linear model without interaction between variables could achieve Rsquared value above 80%, while the model remained unstable. Adding relevant interaction variables improved the model noticeably with stability. However, the model seemed reach its limitation in current configuration when Rsquared near 91%.
  • Ridge, Lasso and Elastic Net Tried various combinations and ranges of lambda and alpha values to find sets of tuning parameters. This process was in some way experimental due to the results were based on the combined effect of the seed value for randomness, the starting and the end points of lambda and alpha, and the step size. In Elastic Net, although various settings resulted in various sets of turning parameters, the overall Rsquared values of the elastic model remained stable.
  • Model Comparisons Comparing the four models: Linear, Ridge, Lasso, and Elastic Net showed Lasso was influential and largely adopted by Elastic Net in the developed model.
  • Predictions Although the main objective of the project was to examine and analyze linear regression and not necessarily engineer for a high Kaggle score. Submissions made resulted to .014 range with predictions made by the Elastic Net model.

Data Analysis


Kaggle House Price Dataset

Downloaded and imported the train data set. Here’s some information by examining the structure and the summary.

[1] “Imported train data set:  1460  obs. of  81 variables”


Next examined the distribution of missingness and the percentage of missing values. There were a few variables with most observations missing, which made these variables not usable and they were consequently removed. Here’s a visualization of missingness of the train data set.

Percentage of Missing Values

Further examination of the percentage of missing values of each variable revealed:

Feature Selection


Removed a set of variables at this time based on:

  • a large percentage of missing values which made a variable not usable
  • feature importance confirmed by Boruta
  • a consistent insignificant level of p-value as a predictor in test runs


After having converted all variables in train dataset to integer or numeric fields, programmatically imputed the data for missing values, I ran Boruta to initially analyze the importance of variables. And it took about 40 minutes in the context to iterate 500 times and produced something like the following results where those in green were with confirmed importance, while red rejected, i.e. not important features. The yellow ones were tentative which were not yet resolved before reaching the set number of iterations.

Stored the list of features confirmed by Boruta and subsequently removed these features not included in this list from the train dataset.

Features with Insignificant P-Values

While developing, fitting, and tuning the model, I documented a list of features consistently with insignificant p-values, i.e. greater than 0.05, in test runs. Below is a snapshot of these features to be removed form train dataset prior to executing a test run. Notice these features were not a unique set and various development paths and configurations could and would result a different set of features.

Character Variables

Factor variables were read in as character ones. Rather than converting into factor variables, they were converted into integer or numeric fields for later imputing data as well as deriving feature importance programmatically.

The above, for example, showed the variable, BldgType, was a character variable with five unique levels. It was converted into an ordinal one with values between 1 and 2. Notice that the process was iterative during data preparation and feature engineering. Both converting and combining variables were considered. Domain knowledge, subjectivity, and common sense were all relevant to the what and how to convert a variable, as applicable. The technique and strategies can and will vary from person to person and model to model.

Numeric Variables

For numeric variables, their values can produce unintended effects. For instance, assume modeling a house price having a linear relationship with the month a house is sold. In such case, a generalization is essentially inherited into the model, that a house sold in December with a value of 12 would contribute 12 times more to a response variable than one sold in January with a value of 1. This configuration fundamentally does not correctly reflect the seasonality, nor the degree of impact on a house price based on the month a house is sold.

One alternative way of modeling seasonality is, as shown above, to convert the variable values to a scale between 0 and 1 where in the summer, i.e. July to September, with the most weight contributing to the market house price, the response variable, and in the winter time the least weight to signify the slow period.

Later, this feature was removed from the final model due to insignificance consistently denoted by p-values in a series of test runs. Still, it was necessary to make the effort to prepare the data and convert this variable, from a January-to-December as 1-to-12 scale to a more meaningful and realistic one for describing real-world scenarios. With a proper scale of this and other similar variables, packages like mice could calculate meaningful values for imputation and Boruta for deriving feature importance.

Above all, the strategies to determine what and how to convert a variable have much to do with an examiner’s domain knowledge, subjectivity, and common sense in addition to reviewing the composition and distribution of the data.

And the values of a variable sometimes do not tell the whole story. It may not be the values of a variable, but the variance of those values plays a more influential role for making predictions.

Data Visualization


Up to this time, I had an initial set of features to start working on developing a model. Throughout the development, I would make changes of the feature set and observations based on diagnostics of the test results. The presented series of plots were generated along the development process.

Along the development, I produced multiple versions and configurations of the following plots. The set presented here is just one of the many.

Prepared Train Dataset

Here’s a snapshot of the prepared data set ready for Machine Learning development.


Distribution of the Label

The label, i.e. response variable, was SalePrice, here plotted without logarithm.


Label vs. Feature

To examine a feature relevant to the label, SalePrice, plotted each pair individually. The linearity among variables was obvious.



Here’s a pairs.panels plot with all features and the label. This plot gives an overview of the linearity between variables and the variance of individual variables.

Correlation Matrix

These three plots: correlation matrix, label vs. feature, and pairs.panels were my main references for developing an initial model.



Partitioning Data

I partitioned the train dataset into a 70-30 split where 70% for training and 30% for testing. Here is a set of plots produced by fitting the four regression models: Linear, Ridge, Lasso, and Elastic Net.


1. Linear Model


Here’s a summary of lm for one of the runs. The adjusted R-squared was 0.9067 with insignificant features removed.


1.1 Diagnostic Plots

The diagnostic plots played an important role in the initial development. Form the Residuals vs. Fitted plot, there seemed some nonlinearity. Many changes and adjustment made were based on examining and interpreting these plots. In each iteration, I reviewed the plots and changed the composition of features and interactions, removed outliers or added back observations, etc. followed by more test runs. The process was highly iterative and the productivity relied much on well documentation to facilitate the analysis and restore a configuration when needed.


1.2 Variable Importance and Distribution of Residuals


1.3 Predicted vs. Observed



  1. 2. Ridge Regression

    Set alpha=0 and a sequence for tuning Lambda. I started from a wide range like 0.001 to 100 and gradually reduced the range to find a good window. The size of a step sometimes had a noticeable effect on the outcome. Many experimentation and repetitions happened here.

    2.1 Regularization









2.2 Variable Importance and Distribution of Residuals



2.3 Predicted vs. Observed



3. Lasso Regression

Set alpha=1 and a sequence for tuning Lambda. Like what I did in Ridge Regression, I started from a wide range and gradually reduced to and identified a good range and step to scan.

3.1 Regularization









3.2 Variable Importance and Distribution of Residuals


3.3 Predicted vs. Observed


4. Elastic Net

Initially I set one sequence for tuning both alpha and lambda. This turned out not productive for me. Since in a configuration the two values were far apart from each other, the range for scanning would become relatively extensive with a small step sometimes necessary to initially locate the values. A few times my laptop would run out of resources and simply not responding later in a run.

Setting an individual sequence for alpha and Lambda was a more productive approach for me. Nevertheless, the increased combinations and with 10-fold cross validation, it took longer and a few iterations to narrow the ranges and locate the best set of alpha and lambda.

4.1 Regularization

With these many features, overfitting would be likely as these plots revealed.



4.2 Variable Importance and Distribution of Residuals


4.3 Predicted vs. Observed


Model Comparisons


Other than Ridge Regression, the rest three performed very much at the same level.

Summary of Models


Predicted vs. Observed

Placing all four models together, Elastic Net apparently favored Lasso Regression and the pattern are almost identical. While Linear, Lasso, and Elastic Net all have a very similar pattern, the color nevertheless shows there were subtle differences in density.


Closing Thoughts


Considering this model employed just multiple linear regression, I was surprised that the scores turned out to be higher than expected, based on a few submissions I have done. Linear regression is conceptually simple and relevant to many activities happening in our daily life. We all do linear regression in our mind when making a purchase. Is this expensive or cheap? Every time, we ponder that thought, we are doing linear regression in some shape and form.

We must however not mistakenly and carelessly assume linear regression is as simple as it appears, as I have learned from my own mistake. There is much to investigate and learn from linear regression. Ordinary Least Square (OLS) which linear regression is built upon is too fundamental to overlook. The simplicity of OLS offers a clear strategy and enables Machine Learning algorithms to describe the combining effects of a set of predictors based on the distance. The concept of residuals is simple, approach straightforward, and objective clear. Ultimately, we want to minimize the distance of what is observed and what is predicted. This distance is our cost or error function.

There are a few options to continue the development. Tree-based models, ensemble learning, further refining and optimizing the data, more feature engineering, etc. are all applicable. With these many variables, a tree-based model should have a good story to tell. Which is what I plan to try next.

<More on Yung>

Feature Selection with Help from Boruta


When developing a Machine Learning model, if there is a significant number of features to inspect, an initial and manual Exploratory Data Analysis may become tedious and nonproductive. One option is to facilitate the process by testing and identifying important variables based on statistical methods to help trim down features. And that is where Boruta comes in place.


A forest spirit in the Slavic mythology, Boruta (also called Leśny or Lešny) was portrayed as an imposing figure, with horns over the head, surrounded by packs of wolves and bears. Fortunately, in R Boruta is a helpful package for facilitating a feature selection process. Here’s a description from the documentation:

Boruta (CRAN) is an all relevant feature selection wrapper algorithm, capable of working with any classification method that output variable importance measure (VIM); by default, Boruta uses Random Forest. The method performs a top-down search for relevant features by comparing original attributes’ importance with importance achievable at random, estimated using their permuted copies, and progressively eliminating irrelevant features to stabilize that test.


The following is a sample routine in R demonstrating how I used Boruta to find a starting point for features selection. Some noticeable settings include:

  • The input data was train.imp.
  • doTrace=2 will log the activities and show progress to console.
  • maxRuns is how many times Boruta should run. In some circumstances (too short Boruta run, unfortunate mixing of shadow attributes, tricky dataset. . . ), Boruta may leave some attributes Tentative. For my particular case, the first 100 runs (which is a good initial value to start) confirmed most of the features with a few remain tentative. And I set it to 500 to finally resolve 80 features I was interested in and it took about half an hour.
  • TentativeRoughFix performs a simplified, weaker test for judging such attributes. This function should be used with discretion, since this weak test can lower the confidence of the final results.
  • getSelectedAttributes does what it sounds like.
  • attStats keep the statistics and the result of each resolved variable.


train.boruta <- Boruta(SalePrice~., data=train.imp, doTrace=2, maxRuns=500)

plot(train.boruta , las=2, cex.axis=0.7, xlab='')

train.boruta.fix <- TentativeRoughFix(train.boruta)
train.boruta.selected.features <- getSelectedAttributes(train.boruta.fix, withTentative = F)


train.boruta.selected.features.stats <- attStats(train.boruta.fix)
saveRDS(train.boruta.selected.features.stats, 'boruta/train.boruta.selected.features.stats.rds')

Also included are plots of Boruta output and attStats. Those confirmed important were in green and rejected in red. Unresolved variables were in yellow and classified as tentative which Boruta was not able to conclude their importance. And attStats kept and reported the statistics associated with the decisions.

Boruta uses Random Forest algorithm to provide educated sets of important and not so important features, respectively. Not only save time, but offer a repeatable and automatic way for initial exploratory data analysis.

Closing Thoughts

Feature selection is a critical task in developing a Machine Learning model. Extraneous features introduce multicollinearity, increase variance and lead to overfitting. Data is everything and feature selection is as critical. This is a task that can consume much of model development time. And for me, making the routine a code snippet and getting the mechanics in place help me become productive much quicker. A next logical step is to programmatically consume and integrate Boruta output to build and train a preliminary Machine Learning model to possibly establish a baseline of a target algorithm. Stay tuned for that.

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