Aegis-GPU4.5 Relationships, trends and confounders
Spotting patterns and the factors that can distort results.
Key points
NVIDIA warns that R² does not measure bias, so an overfitted model can have a high R², and you should also look at other metrics.
What NVIDIA says (1)
“Second, R² does not give any measure of bias, so you can have an overfitted (highly biased) model with a high value of R².”
NVIDIA notes that data from a single institution can be biased by patient demographics, instruments or clinical specializations, which can affect the results of a model trained on it.
What NVIDIA says (1)
“Medical institutions have had to rely on their own data sources, which can be biased by, for example, patient demographics, the instruments used or clinical specializations.”
Cross-filtering replaces hand-written DataFrame queries with a graphical user interface (GUI). In NVIDIA's example, a clear pattern emerged between weekday and weekend trips.
What NVIDIA says (2)
“As shown in Figure 5, a clear pattern emerges between weekday and weekend trips”
“This approach replaces dataframe queries with a GUI tool.”
Sample question
A model has a high R² on your data. Why might that still mislead you?
Show the answer
Answer: R² gives no measure of bias, so an overfitted model can still show a high R²; look at other metrics too
NVIDIA warns that R² does not measure bias, so an overfitted model can have a high R², and you should also look at other metrics.
What NVIDIA says (1)
“Second, R² does not give any measure of bias, so you can have an overfitted (highly biased) model with a high value of R².”
Practice 4.5 (3 questions) Full Data Analysis and Visualization guide
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