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Flashcard 7715437546764

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#tensorflow #tensorflow-certificate
Question
F1-score

Combination of precision and [...], ususally a good overall metric for classification models.

Answer
recall

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F1-score Combination of precision and recall, ususally a good overall metric for classification models.

Original toplevel document

TfC_02_classification-PART_2
anced classes Precision For imbalanced class problems. Higher precision leads to less false positives. Recall Higher recall leads to less false negatives. Tradeoff between recall and precision. <span>F1-score Combination of precision and recall, ususally a good overall metric for classification models. keyboard_arrow_down 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.metr






Annotation 7715439906060

#tensorflow #tensorflow-certificate

Three types of classification problems:

  • binary classification
  • multiclass
  • multilabel
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TfC_02_classification-PART_1
Types of classification problems Three types of classification problems: binary classification multiclass multilabel Multilabel classification - a sample can be assigned to more than one label from more than 2 label options Multiclass classification - a sample can be assigned to one label but from mor