**English – SmarterPoland.pl**, and kindly contributed to R-bloggers)

The next DALEX workshop will take place in 4 days at UseR. In the meantime I am working on a new explainer for a single observation.

Something like a diagnostic plot for a single observation. Something that extends Ceteris Paribus Plots. Something similar to Individual Conditional Expectation (ICE) Plots. An experimental version is implemented in ceterisParibus package.

**Intro**

For a single observation, Ceteris Paribus Plots (What-If plots) show how predictions for a model change along a single variable. But they do not tell if the model is well fitted around this observation.

Here is an idea how to fix this:

(1) Take N points from validation dataset, points that are closest to a selected observation (Gower distance is used by default).

(2) Plot N Ceteris Paribus Plots for these points,

(3) Since we know the true y for these points, then we can plot model residuals in these points.

**Examples**

Here we have an example for a random forest model. The validation dataset has 9000 observations. We use N=18 observations closest to the observation of interest to show the model stability and the local goodness-of-fit.

(click to enlarge)

The empty circle in the middle stands for the observation of interest. We may read its *surface* component (OX axis, around 85 sq meters), and the model prediction (OY axis, around 3260 EUR).

The thick line stands for Ceteris Paribus Plot for the observation of interest.

Grey points stands for 18 closest observations from the validation dataset while grey lines are their Ceteris Paribus Plots.

Red and blue lines stand for residuals for these neighbours. Corresponding true values of y are marked with red and blue circles.

Red and blue intervals are short and symmetric so one may say that the model is well fitted around the observation of interest.

Let’s see an another example for the same model but a different point prediction. This time for a large apartment.

(click to enlarge)

Residuals are larger, all negative (true values are higher than predictions). The local goodness-of-fit for the model is pretty bad. Other diagnostic tools (see auditor) show that for this dataset the random forest model is heavily biased for expensive apartments.

**Wangkardu**

Side story: In AGNSW I found a painting named *Wangkardu* that looks (to me) like above graphs. If you want to use its colors, just add *palette = „wangkardu”* to the plot function (R code here).

(click to enlarge)

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**English – SmarterPoland.pl**.

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