fairmodels: let’s fight with biased Machine Learning models (part 1 — detection) Author: Jakub Wiśniewski TL;DR The fairmodels R Package facilitates bias detection through model visualizations. It implements few mitigation strategies that could reduce the bias. It enables easy to use checks for fairness metrics and comparison between different ...

Introduction On July 26, 2020, Propublica released a dataset on police discipline records. I wanted to get a look at the data and do some exploratory analysis.

library(tidyverse)

## ── Attaching packages ──────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.0
## ✓ tidyr   1.1.0     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## Warning: package 'ggplot2' was built under R version 3.6.2

## Warning: package 'tibble' was built under R version 3.6.2

## Warning: package 'tidyr' was built under R version 3.6.2

## Warning: package 'purrr' was built under R version 3.6.2

## Warning: package 'dplyr' was built under R version 3.6.2

## ── Conflicts ─────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(gt)

## Warning: package 'gt' was built under R version 3.6.2

library(here)

## here() starts at /Users/samportnow/sam-portnow-website

library(fs)

## Warning: package 'fs' was built under R version 3.6.2

library(skimr)

## Warning: package 'skimr' was built under R version 3.6.2

library(janitor)

## Warning: package 'janitor' was built under R version 3.6.2

## 
## Attaching package: 'janitor'

## The following objects are masked from 'package:stats':
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##     chisq.test, fisher.test

Read in Data

data = read_csv(here::here('content', 'post_data', 'allegations_20200726939.csv'))

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In our last post, we ran through a bunch of weighting scenarios using our returns simulation. This resulted in three million portfolios comprised in part, or total, of four assets: stocks, bonds, gold, and real estate. These simulations relaxed the allocation constraints to allow us to exclude assets, yielding a ...

In our last post, we ran through a bunch of weighting scenarios using our returns simulation. This resulted in three million portfolios comprised in part, or total, of four assets: stocks, bonds, gold, and real estate. These simulations relaxed the allocation constraints to allow us to exclude assets, yielding a ...

Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso).

Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso).

In our last post, we ran through a bunch of weighting scenarios using our returns simulation. This resulted in three million portfolios comprised in part, or total, of four assets: stocks, bonds, gold, and real estate. These simulations relaxed the allocation constraints to allow us to exclude assets, yielding a ...

Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso).

Daniel Hanson is a full-time lecturer in the Computational Finance & Risk Management program within the Department of Applied Mathematics at the University of Washington. In the previous post in this series, we looked at some design considerations when integrating standard and reusable C++ code into an R package. Specific emphasis ...

The INLA Approach to Bayesian models The Integrated Nested Laplace Approximation, or INLA, approach is a recently developed, computationally simpler method for fitting Bayesian models [(Rue et al., 2009, compared to traditional Markov Chain Monte Carlo (MCMC) approaches. INLA fits models that are classified as latent Gaussian models, which are applicable ...

We’re excited to announce the release of rfm 0.2.2 on CRAN! rfm provides tools for customer segmentation using Recency Frequency Monetary value analysis. It includes a Shiny app for interactive segmentation. You can install rfm with:

install.packages("rfm")

In this blog post, we will summarize the changes implemented in the current (0.2.2) ...

") training_loop_vae(ds_train) test_batch % iter_next() encoded % round(5)) } ``` Experimental setup and data The idea was to add white noise to a deterministic series. This time, the Roessler system was chosen, mainly for the prettiness of its attractor, apparent even in its two-dimensional projections: (#fig:unnamed-chunk-1)Roessler attractor, ...

You want to use the R programming language to transform data into strategic knowledge? You are looking for the optimal introduction to work with R? You would like to participate in a training that really helps you despite your home office and limited travel possibilities? Then register for our online ...