wehub-resource-sync
4 lines
821 B
TeX
4 lines
821 B
TeX
Streamlining workflows with pipelines
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Loading the Breast Cancer Wisconsin dataset
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Combining transformers and estimators in a pipeline
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Using k-fold cross-validation to assess model performance
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The holdout method
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K-fold cross-validation
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Debugging algorithms with learning and validation curves
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Diagnosing bias and variance problems with learning curves
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Addressing overfitting and underfitting with validation curves
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Fine-tuning machine learning models via grid search
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Tuning hyperparameters via grid search
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Algorithm selection with nested cross-validation
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Looking at different performance evaluation metrics
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Reading a confusion matrix
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Optimizing the precision and recall of a classification model
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Plotting a receiver operating characteristic
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The scoring metrics for multiclass classification
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Summary |