At Wayfair, technology and data expertise enable data scientists to transform new web datasets into intelligent machine algorithms that re-imagine how traditional commerce works. In this post, we introduce how visual tools like Violin Plots amplify our data acumen to unlock deep insights. The savvy data scientist recognizes the value of a Violin Plot when engineering new model features. We share how this method is applied in an e-commerce example where fuzzy text matching systems are developed to identify similar products sold online.

Key article takeaways:

  • Skillful usage of Violin Plots can improve feature engineering and selection/
  • A good Violin Plot communicates more information about data irregularities than standard summary statistics and correlation coefficients
  • Code and data are available on Github

Good data visualizations are helpful at every step of a data science project. When starting out, good data visualizations can inform how one should formulate the problem. Visualizations also can help guide decisions surrounding which data inputs to use, and are helpful when evaluating model accuracy and feature importance. When debugging an existing model, visualizations help diagnose data irregularities and bias in model predictions. Finally, when communicating with business stakeholders, the right visualization makes a clear point without any additional explanation.

One data visualization that is particularly helpful when working on binary classification problems is the split violin plot. In my experience, this is a type of plot that is not nearly as famous as it should be. In brief, a split violin plot takes a variable grouped by two categories and plots a smoothed histogram of the variable in each group on opposite sides of a shared axis.

Figure 1: An example of a split violin plot, which has two distributions plotted along a shared axis. The distributions are typically for the same variable, but differ along some categorical dimension (in this case, binary case 1 or binary case 2). 

What I like most about violin plots is that they show you the entire distribution of your data. If data inputs violate your assumptions (e.g. multimodal, full of null values, skewed by bad imputation or extreme outliers) you see the problems at a quick glance and in incredible detail. This is better than a few representative percentiles as in a box and whisker plot, or a table of summary statistics. They avoid the problem of oversaturation prevalent in scatter plots with lots of points, and reveal outliers more clearly than you would in a histogram without a lot of fine-tuning.

We’ll illustrate these advantages in a simple example where we use fuzzy string matching to engineer features for a binary classification problem.

An Example using NLP to Identify Similar Products

At Wayfair, we develop sophisticated algorithms to parse large product catalogs and identify similar products. Part of this project involves engineering features for a model which flags two products as the same or not. Let’s start from a dataset that provides several pairs of product names and a label indicating whether or not they refer to the same item.[1]

Product1 Product2 Match
Da-Plex Rigid Rear Black Fixed Frame Projection Screen Kohler K527E1SN DTV Prompt Shower Interface with ECO Mode False
Ruby Coronet Oval Platter 16″ Ruby Coronet 1 Door Outdoor Enclosed Bulletin Board Size: 3′ H x 2′ W False
Lower Case Letter Painting Print on Wrapped Canvas 28″ x 28″ – Yel… Letter – Lower Case ‘p’ Stretched Wall Art Size: 28″ x 28″ True
67″ x 29.5″ Soaking Bathtub Kit Fanmats Alabama State Football Rug 20.5″x32.5″ False

Fuzzywuzzy Similarity Scores

For the purpose of this fuzzy text matching illustration, let’s use an open-source Python library called fuzzywuzzy (developed by the fine folks at SeatGeek). This library contains several functions for measuring the similarity between two strings. Each function takes in two strings and returns a number between 0 and 100 representing the similarity between the strings. Functions differ in their conventions and different functions will produce different similarity results.

from fuzzywuzzy import fuzz

fuzz.QRatio(‘brown leather sofa’, ’12ft leather dark brown sofa’)
>>> 57
fuzz.WRatio(‘brown leather sofa’, ’12ft leather dark brown sofa’)
>>> 86
fuzz.token_set_ratio(‘brown leather sofa’, ’12ft leather dark brown sofa’)
>>> 100

It’s rarely obvious which function is best for a given problem. Let’s consider five different fuzzy matching methods and compute similarity scores for each pair of strings. Using these scores, we’ll create some violin plots to determine which method is best for distinguishing between matches and not matches. (You could also consider combinations of scores, though this comes at a higher computational cost.)

Product1 Product2 Match QR WR part set sort
Da-Plex Rigid Rear Black Fixed Frame… Kohler K527E1SN DTV Prompt Shower… False 33 36 30 37 34
Ruby Coronet Oval Platter… 1 Door Outdoor Enclosed Bulletin… False 33 34 31 32 36
Lower Case Letter Painting Print… Letter – Lower Case ‘p’ Stretched Wall… True 45 64 50 67 59
67″ x 29.5″ Soaking … Fanmats Alabama State Football… False 24 41 27 42 43

A few lines of code are all we need to generate split violin plot using the Seaborn library. The purple distribution depicts a smoothed (sideways) histogram of fuzzy matching scores when Match is True, while the light-green shows the distribution of similarity scores when Match is False. When two distributions have little or no overlap along the y-axis, the fuzzy matching function will do a better job distinguishing between our binary classes.

Figure 2: These violin plots depict “fuzzy similarity scores” for pairs of product names. Pairs that refer to the same product are labeled as “True” and are otherwise labeled as “False.” The distributions vary depending on the fuzzywuzzy method used to compute the scores. 

Generally, these fuzzy matching scores do a good job in distinguishing between observations where the two names refer to the same product. For any method, a pair of names with a similarity score of 50 or more will probably refer to the same product.

Still, we can see that some fuzzy matching functions do a better job than others in distinguishing between True and False observations. The token_set_ratio plot seems to have the least overlap between the True and False distributions, followed by the plots for token_sort_ratio and WRatio. Of our five similarity scores, the scores from these methods should perform the best in any predictive model. In comparison, notice how much more the True and False distributions overlap for the partial_ratio and QRatio methods. Scores from these methods will be less helpful as features.

Conclusion: Violin plots suggest that of our five similarity scores, token_set_ratio would be the best feature in a predictive model, especially compared to the partial_ratio or QRatio methods.

Why Violin Plots are Superior to More Conventional Analyses

For comparison, let’s look at the Pearson correlation coefficients between our fuzzy-matching scores and our indicator variable for whether the pair is a match or not.

  QRatio WRatio partial_ratio token_set_ratio token_sort_ratio
Match 0.68 0.80 0.71 0.84 0.76

For this data, the correlation coefficients give a similar ranking as achieved using the violin plots. The token_set_ratio method gives the strongest correlation to the Match variable while the QRatio method gives the weakest correlation. If our goal was only to identify the best fuzzywuzzy function to use, we apparently could have made our selection using correlation coefficients instead of violin plots. In general, however, violin plots are much more reliable and informative. Consider the following (pathological) example.

Figure 3: Violin plots will show when/where your data violate your expectations. This isn’t necessarily true of other visualizations. 

In these violin plots, the similarity scores on the left appear to be more helpful in separating between matches and not-matches. There is less overlap between the True and False observations and the observations are more tightly clustered into their respective groups.

However, notice that the relationship between the similarity scores and the True/False indicator is not at all linear or even monotone. As a result, correlation coefficients can fail to correctly guide our decision on which set of scores to use. Is this true? Let’s take a look.

  score1 score2
Match -0.28 -0.30

Here, the correlation coefficients of score1 and score2 against the outcome variable are quite close. However, the plot on the right –the one that doesn’t cleanly separate True and False observations– has the stronger correlation coefficient. If we blindly took the series with the strongest correlation, we would choose the less helpful of the two features.


Wrapping up…

To summarize:

  1. Split violin plots are a great way to visualize your data at a quick glance, especially when dealing with binary classification problems.
  2. Violin plots can guide us in feature engineering and selection by revealing the variables that best separate the two classification outcomes.
  3. Correlation coefficients, in comparison, can accomplish a similar task when the relationship between the features and labels are linear. When linearity is violated, the correlation coefficients are misleading in comparison to the violin plots.

There are certainly limits to this approach. Nothing that requires an “eye test” is scalable to many features. Also, violin plots have a few important parameters which, if not properly set, can hide important patterns in the data. Still, when properly used, split violin plots are a great tool for binary classification type problems.


Special thanks to Zhenyu Lai, Aditya Karan, Brad Fay and Laura Tengelsen for excellent editing and feedback.

Authors: Benjamin Tengelsen

[1] Data and code for this post are available on github: