Comments (3)
Thanks @alanocallaghan, these are all great points.
It is extremely weird to drop the categorical outcome variable and use it as y, including the encoded numeric variable in x. I realise this is an intro lesson but this seems to me a coding mistake that would be common for novices
Yes, this is a definite bug!
from machine-learning-novice-python.
From evaluation:
metrics.plot_roc_curve(reg, x_test, y_test)
no longer works
from sklearn.metrics import RocCurveDisplay
reg_disp = RocCurveDisplay.from_estimator(reg, x_test, y_test)
from machine-learning-novice-python.
Bootstrap episode KDE plot claims to be on the test set, but it's actually using training
from machine-learning-novice-python.
Related Issues (10)
- Adding learner profiles to guide curriculum
- Definitions of sensitivity and specificity HOT 1
- Use of "normal" to describe patients HOT 2
- Consider moving bootstrap section to machine-learning-trees-python
- Add section on regularisation?
- Clarify number of samples taken in the resample function
- Relabel y-axis as "frequency" in the bootstrapping plots
- Add calibration to the evaluation section
- Add section on regularization (perhaps L1 Lasso regression)
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from machine-learning-novice-python.