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Ch. 19 Interpret Model (19.2 - 19.5 (The overall impact of a feature…
Ch. 19 Interpret Model
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19.2 - 19.5
The overall impact of a feature without consideration for the impact of other features treats each feature as a standalone effect on the target
A full green bar indicates that the feature explains at least 30% of the target value, and, hovering the cursor over the green bar, the optimization measure score for the individual feature model becomes visible
The importance score is exceedingly useful because it allows a data scientist to focus attention on the features most likely to yield additional predictive value if misinterpreted by the AutoML, such as through misinterpretation of the variable type
DataRobot does this by randomly shuffling the values of one feature within the validation data segmen
A value of 1 second is now assigned to the left arm. The next step is to reset his left arm to its original specifications and randomly scramble his right arm
There is also a valuable lesson immediately present here, which is that for the models that accessed the text embedded in the diagnosis descriptions (diag_1_desc, diag_2_desc, and diag_3_desc), this embedded information was among the most important information available for the ENET Blender
this chart was developed using only 1/4th of the train data, confidence in the conclusions displayed in this pane remains low
as soon as the new model has finished running, examine its LogLoss score by searching the page for the model number once more for BP17—the blueprint number for both models
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he left Y-axis contains the frequency of cases in the validation set. The X-axis contains the values of the most predictive feature, discharge_disposition_id
The sixth bar, representing Discharged/transferred to another rehab facility including rehab units of a hospital, may offer an enticing indicator that rehabilitation works and that patients who are sent into rehab improve enough that their stats “fool” the model
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