The slicing syntax for tuples is identical to that of lists, but because tuples are immutable, slicing a tuple creates a new tuple rather than modifying the original one. Python Tuplesare similar to lists in many ways, but with one key difference: tuples are immutable, meaning they cannot...
WeakSpot: identification of weak regions with high residuals by slicing techniques. Overfit: identification of overfitting regions according to train-test performance gap. Reliability: assessment of prediction uncertainty by split conformal prediction techniques. ...
If you are new to Python you may run into issues like this. In Python if you say a = [10] And then say b = a What happens? b gets the value of a, but also any change in a will make the similar change in b, because instead of just copying the value "b" is pointed ...
To understand how a single feature effects the output of the model we can plot the SHAP value of that feature vs. the value of the feature for all the examples in a dataset. Since SHAP values represent a feature's responsibility for a change in the model output, the plot below represents...
Each letter is assigned a numeric value, so a word or sentence is really a series of numbers. When you cut some text out of one paragraph and paste it into a different one, you're really slicing a chunk of numbers from a long list and moving them higher up (or lower down the list...
Bump ini from 1.3.5 to 1.3.8 in /javascript Dec 12, 2020 notebooks Add Multioutput Regression Example Mar 5, 2021 shap Merge pull requestshap#1830from morriskurz/master Mar 5, 2021 tests Fix slicing issues and Text masking with Permutation explainer ...
javascript Bump ini from 1.3.5 to 1.3.8 in /javascript Dec 12, 2020 notebooks Fix typos and add more clustering util support. May 18, 2021 shap Fix typos and add more clustering util support. May 18, 2021 tests Fix slicing issues and Text masking with Permutation explainer Mar 4, 2021 ...
If we take many force plot explanations such as the one shown above, rotate them 90 degrees, and then stack them horizontally, we can see explanations for an entire dataset (in the notebook this plot is interactive): # visualize all the training set predictionsshap.plots.force(shap_values)...
If we take many force plot explanations such as the one shown above, rotate them 90 degrees, and then stack them horizontally, we can see explanations for an entire dataset (in the notebook this plot is interactive): # visualize all the training set predictionsshap.plots.force(shap_values)...