The size of parts are not too similar to each other Relative differences are more important than exact values Proportions are static (single time point) Limitations of donut charts Too many parts make donut charts difficult to interpret (shouldn’t be more than 5 parts) ...
All important confounding variables must be included. This is a strong assumption of causal inference analysis, and it means that if any variables that are related to both the exposure and outcome variables are not included as confounding variables, the estimate of the causal effect will b...
A violin plot distribution based on gender for a distribution of total bill amounts per day. | Image: Eryk Lewinson In this article, I showed what are the violin plots, how to interpret them and what their advantages are over the boxplots. One last remark worth making is that the box...
This has less to do with the shortcomings of the models, and more to do with the shortcomings of human cognition. Often, the higher the predictive accuracy of a model, the harder it is to interpret its inner workings. This is where interpretability techniques come into play by providing a ...
7 We need to do this as otherwise mothers who engage in no action would be eliminated from the estimation and not be assigned any style. The idea is that we interpret the absence of an action as a manifestation as well. Draca and Schwarz (2021) follow a similar approach and subsequently...
In response to the second research question, Section 4.2 unfolds the underlying relationships of identified topics through correlation analysis and performs regression analysis to find the determinant factors. Section 4.3 interprets the sentiment changes based on word analysis. For consistency, the ...
For ex- ample, Apple's Siri and Google Now rely on natural user interfaces to recognise spoken words, interpret their meanings, and act on them accordingly. More- over, a company called SmartAction now provides call computerisation solu- tions that use ML technology and advanced speech ...
If there are important confounding variables that are not available, interpret the results with extreme caution or do not use the tool. The correlations between the confounding variables and the exposure variable must be removed in order to isolate the causal effect. In causal inference a...
the scatterplot, and larger bubbles indicate that the feature had a larger balancing weight and contributed more to the estimation of the ERF. For propensity score matching, if the observation has no matches, it is drawn as a light gray point. Trimmed observations are not shown in the...
If there are important confounding variables that are not available, interpret the results with extreme caution or do not use the tool. The correlations between the confounding variables and the exposure variable must be removed in order to isolate the causal effect. In causal infer...