Partial Dependence (PD) Plot
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A Partial Dependence (PD) Plot is a plot which shows the marginal effect that one or two predictor features have on the predicted outcome of a machine learning model (model's predictions).
- Context:
- It can show whether the relationships are linear, monotonic or more complex.
- See: Feature Relevance Measure.
References
2018
- https://www.kaggle.com/dansbecker/partial-dependence-plots
- QUOTE: ... Partial dependence plots show how each variable or predictor affects the model's predictions. This is useful for questions like:
- How much of wage differences between men and women are due solely to gender, as opposed to differences in education backgrounds or work experience?
- Controlling for house characteristics, what impact do longitude and latitude have on home prices? To restate this, we want to understand how similarly sized houses would be priced in different areas, even if the homes actually at these sites are different sizes.
- Are health differences between two groups due to differences in their diets, or due to other factors?
- If you are familiar with linear or logistic regression models, partial dependence plots can be interpreted similarly to the coefficients in those models. But partial dependence plots can capture more complex patterns from your data, and they can be used with any model. ...
- QUOTE: ... Partial dependence plots show how each variable or predictor affects the model's predictions. This is useful for questions like:
2017
- (Scikit Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble Retrieved:2017-10-22
- QUOTE: ... Partial dependence plots for tree ensembles.
ensemble.partial_dependence.partial_dependence(…), Partial dependence of target_variables.ensemble.partial_dependence.plot_partial_dependence(…), Partial dependence plots for features.
- QUOTE: ... Partial dependence plots for tree ensembles.
2016
- (Jeongan et al., 2016) ⇒ Jig H. Jeongan, Jonathan P. Resop, Nathaniel D. Mueller, David H. Fleisher, Kyungdahm Yun, Ethan E. Butler, Dennis J. Timlin et al. (2016). “Random Forests for Global and Regional Crop Yield Predictions.” PLoS One 11, no. 6
- QUOTE: … The second analysis tool, partial dependence plot, shows how the RF model predictions are influenced by each predictor when all of the other predictors in the model are being controlled. The Y-axis value of a partial dependence …
2015
- https://christophm.github.io/interpretable-ml-book/pdp.html
- QUOTE: The partial dependence plot (short PDP or PD plot) shows the marginal effect one or two features have on the predicted outcome of a machine learning model (J. H. Friedman 2001). A partial dependence plot can show whether the relationship between the target and a feature is linear, monotonic or more complex. For example, when applied to a linear regression model, partial dependence plots always show a linear relationship. ...
2001
- (Friedman, 2001) ⇒ Jerome H. Friedman. (2001). “Greedy function approximation: A gradient boosting machine.” In: Annals of Statistics, 2001. 1189-1232.