Keyword Analysis & Research: sklearn metrics
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3.3. Metrics and scoring: quantifying the quality of predicti…
https://scikit-learn.org/stable/modules/model_evaluation.html
Metric functions: The sklearn.metrics module implements functions assessing prediction error for specific purposes. These metrics are detailed in sections on Classification …
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sklearn.metrics.precision_score — scikit-learn 1.4.1 documentation
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html
sklearn.metrics. precision_score (y_true, y_pred, *, labels = None, pos_label = 1, average = 'binary', sample_weight = None, zero_division = 'warn') [source] ¶ Compute …
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sklearn.metrics.accuracy_score — scikit-learn 1.4.1 documentation
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
sklearn.metrics.accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None) [source] ¶. Accuracy classification score. In multilabel …
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sklearn.metrics.classification_rep…
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html
sklearn.metrics.classification_report(y_true, y_pred, *, labels=None, target_names=None, sample_weight=None, digits=2, output_dict=False, zero_division='warn') …
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3. Model selection and evaluation — scikit-learn 1.4.1 documentation
https://scikit-learn.org/stable/model_selection.html
Metrics and scoring: quantifying the quality of predictions. 3.3.1. The scoring parameter: defining model evaluation rules. 3.3.2. Classification metrics. 3.3.3. Multilabel …
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