wehub-resource-sync
26 lines
1013 B
Markdown
26 lines
1013 B
Markdown
Sebastian Raschka, 2015
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Python Machine Learning - Code Examples
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## Chapter 6 - Learning Best Practices for Model Evaluation and Hyperparameter Tuning
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- 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
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