Explainable AI Cheatsheet Cheat Sheet by kord

Used to see contri­­bu­tions of different atributes for the predic­­tion. These SHAP values are valid for this observ­­ation only. With other data points the SHAP values will change.

shap.p­­lo­t­s.f­­orc­­e(­s­h­ap­­_va­­lu­e­s[0])

Exactly the same purpose as the waterfall chart but much more compact

shap.p­­lo­t­s.f­­orc­­e(­s­h­ap­­_va­­lu­e­s[0])

If you take force plots for all observ­­at­ions, rotate them by 90 degrees and then put next to each other you obtain a SHAP summary plot. This is very useful if you want te see explan­­ations for the entire dataset.

shap.p­­lo­t­s.f­­orc­­e(­s­h­ap­­_va­­lues)

Useful to see which attributes are the most important. For every feature and every sample we plot a dot. We denote value of the feature with color: big (red) or small (blue). On the X-axis we see the import­­ance. From this plot we see that LSTAT is probably the most important attribute. Also, high value of RM increases the model prediction

shap.p­­lo­t­s.b­­ees­­wa­r­m­(s­­hap­­_v­a­lues)

This one is helpful to capture feature intera­­ction and how they influence SHAP value for given feature. On X and Y axis we have inform­­ation about attribute we are interested in. Color represents value of another feature that is intera­­cting with consid­­ered. From here we see that if RAD is small then RM have quite big impact on the prediction whereas when RAD is big then this impact is much smaller.

shap.p­­lo­t­s.s­­cat­­te­r­(­sh­­ap_­­va­l­u­es­­[:,­­"­R­M"], color=­­sh­a­p­_v­­alues)

We can extend this idea to text and see how particular words influence the predic­­tion.

This can be also used for images to see the influence of individual pixels.

This plot shows the decomp­­os­ition of the model's prediction into contri­­bu­tions of different attributes

bd = boston­­_r­f­_­ex­­p.p­­re­d­i­ct­­_pa­­rt­s­(­house, type='­­br­e­a­k_­­down')

bd.plot()

Every attribute is scramled and then based on some evaluation metric (MSE, ACC) we give them scores. Can be visualized on bar chart.

from sklear­n.i­nsp­ection import permut­ati­on_­imp­ortance

r = permut­­at­i­o­n_­­imp­­or­t­a­nc­­e(m­­odel, X, y)

Tree algorithms offer importance scores based on the reduction in the evaluation criterion, like Gini or entropy. Can be used either in regression or classi­­fi­c­ation problems in decision trees, random forests or boosting methods.

px.bar­­(x­=­X.c­­olumns, y=mode­­l.f­­e­at­­ure­­_i­m­p­or­­tan­­ces_)

This figure shows how different attributes in a new instance can change a prediction of the model.
In a nutshell, we held all explan­­atory variables but one (can increase this but comput­­at­ional const increases by much) constant. Then we change the values of one selected and see how the response changes.

cp = boston­_­r­­f­­_e­­­xp.p­­­r­­e­d­­ic­­­t­_p­­­ro­­f­­i­l­e­­­(house)

cp.plo­­­t­(­v­­a­­ri­­­ab­l­­­es­­=­[­­'NOX', 'RM', 'DIS', 'LSTAT'])

After scaling features we can measure how each attribute is important for the model

from sklear­n.l­ine­ar_­model import Linear­Reg­ression

from sklear­n.p­rep­roc­essing import scale

linarModel = Linear­Reg­res­sio­n().fi­t(s­cal­e(X), y)

px.bar­­­(­y­=­­l­­­in­a­­­rM­­o­d­­el.c­­o­­ef_, x=X.co­­­l­umns)