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Flashcard 7768434937100

Parent (intermediate) annotation

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Confusion matrix x-axis -> predicted label

Original toplevel document

TfC_02_classification-PART_2
leads to less false negatives. Tradeoff between recall and precision. F1-score Combination of precision and recall, ususally a good overall metric for classification models. keyboard_arrow_down <span>Confusion matrix Can be hard to use whith large numbers of classes. y-axis -> true label x-axis -> predicted label # Create confusion metrics from sklearn.metrics import confusion_matrix y_preds = model_8.predict(X_test) confusion_matrix(y_test, y_preds) important: This time there is a problem with loss function. In case of categorical_crossentropy the labels have to be one-hot encoded In case of labels as integeres use SparseCategorica

Annotation 7768437034252

#pytest #python #unittest

Beware of float return values!
0.1 + 0.1 + 0.1 == 0.3 Sometimes false

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Beware of float return values! 0.1 + 0.1 + 0.1 == 0.3 Sometimes false assert 0.1 + 0.1 + 0.1 == 0.3, "Usual way to compare does not always work with floats!" Instead use: assert 0.1 + 0.1 + 0.1 == pytest.approx(0.3)

Flashcard 7768438607116

Tags

#pytest #python #unittest

Question

Beware of float return values!
0.1 + 0.1 + 0.1 == 0.3 Sometimes false

assert 0.1 + 0.1 + 0.1 == 0.3, "Usual way to compare does not always work with floats!"

Instead use:

assert 0.1 + 0.1 + 0.1 == [...].approx(0.3)

Answer

pytest

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill