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on 09-Feb-2026 (Mon)

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Flashcard 7791750024460

Tags
#tensorflow #tensorflow-certificate
Question

# Create confusion metrics

from sklearn.metrics import [...]

Answer
confusion_matrix

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# Create confusion metrics from sklearn.metrics import confusion_matrix

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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






Flashcard 7796298747148

Tags
#deep-learning #keras #lstm #python #sequence
Question
Sequence [...] involves predicting a class label for a given input sequence.
Answer
classification

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

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Sequence classification involves predicting a class label for a given input sequence.

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