## Faster Way to Slice Dataframe by Row

When we’d like to slice a dataframe by row, we can employ the split() function or the iter() function in the iterators package. By leveraging the power of parallelism, I...continue reading.

When we’d like to slice a dataframe by row, we can employ the split() function or the iter() function in the iterators package. By leveraging the power of parallelism, I...continue reading.

In the post https://statcompute.wordpress.com/2019/04/27/more-general-weighted-binning, I’ve shown how to do the weighted binning with the function wqtl_bin() by the iterative partitioning. However, the outcome from wqtl_bin() sometimes can be too coarse....continue reading.

I had been asked why I spent so much effort on developing SAS macros and R functions to do monotonic binning for the WoE transformation, given the availability of other...continue reading.

You might be wondering what motivates me spending countless weekend hours on the MOB package. The answer is plain and simple. It is users that are driving the development work....continue reading.

After working on the MOB package, I received requests from multiple users if I can write a binning function that takes the weighting scheme into consideration. It is a legitimate...continue reading.

After wrapping up the function batch_woe() today with the purpose to allow users to apply WoE transformations to many independent variables simultaneously, I have completed the development of major functions...continue reading.

In my GitHub repository (https://github.com/statcompute/MonotonicBinning), multiple R functions have been developed to implement the monotonic binning by using either iterative discretization or isotonic regression. With these functions, we can run...continue reading.

In addition to monotonic binning algorithms introduced in my previous post (https://statcompute.wordpress.com/2019/03/10/a-summary-of-my-home-brew-binning-algorithms-for-scorecard-development), two more functions based on Generalized Boosted Regression Models have been added to my GitHub repository, gbm_bin() and...continue reading.

In my previous post (https://statcompute.wordpress.com/2019/03/10/a-summary-of-my-home-brew-binning-algorithms-for-scorecard-development), I’ve shown different monotonic binning algorithm that I developed over time. However, these binning functions are all useless without a deployment vehicle in production. During...continue reading.

Thus far, I have published four different monotonic binning algorithms for the scorecard development and think that it might be a right timing to do a quick summary. R functions...continue reading.

In past several weeks, I spent a tremendous amount of time on reading literature about automatic parameter tuning in the context of Machine Learning (ML), most of which can be...continue reading.

In the previous post (https://statcompute.wordpress.com/2019/02/03/sobol-sequence-vs-uniform-random-in-hyper-parameter-optimization), it is shown how to identify the optimal hyper-parameter in a General Regression Neural Network by using the Sobol sequence and the uniform random generator...continue reading.

Tuning hyper-parameters might be the most tedious yet crucial in various machine learning algorithms, such as neural networks, svm, or boosting. The configuration of hyper-parameters not only impacts the computational...continue reading.

In the previous post https://statcompute.wordpress.com/2018/07/29/co-integration-and-pairs-trading, it was shown how to identify two co-integrated stocks in the pair trade. In the example below, I will show how to form a mean...continue reading.

In the scorecard development, the area under ROC curve, also known as AUC, has been widely used to measure the performance of a risk scorecard. Given everything else equal, the...continue reading.

In a project of developing PPNR balance projection models, I tried to use the Phillips-Ouliaris (PO) test to investigate the cointegration between the historical balance and a set of macro-economic...continue reading.

In all monotonic algorithms that I posted before, I heavily relied on the smbinning::smbinning.custom() function contributed by Herman Jopia as the utility function generating the binning output and therefore feel...continue reading.

In the post (https://statcompute.wordpress.com/2018/11/23/more-robust-monotonic-binning-based-on-isotonic-regression), a more robust version of monotonic binning based on the isotonic regression was introduced. Nonetheless, due to the loss of granularity, the predictability has been somewhat...continue reading.

Since publishing the monotonic binning function based upon the isotonic regression (https://statcompute.wordpress.com/2017/06/15/finer-monotonic-binning-based-on-isotonic-regression), I’ve received some feedback from peers. A potential concern is that, albeit improving the granularity and predictability, the...continue reading.

In the post (https://statcompute.wordpress.com/2018/11/17/growing-list-vs-growing-queue), it is shown how to grow a list or a list-like queue based upon a dataframe. In the example, the code snippet was heavily relied on...continue reading.