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Title
"Mastering Model Evaluation: A Practical Guide to Machine Learning Metrics and Techniques"
Angličtina
37 b
30 dní na vrácení zboží
"Mastering Model Evaluation for Enhanced Machine Learning" offers an in-depth exploration of model evaluation in machine learning. This comprehensive guide covers essential topics, including classification and regression metrics, confusion matrices, precision-recall trade-offs, ROC and AUC analysis, cross-validation, overfitting, hyperparameter tuning, model selection, class imbalance handling, and ethical considerations. It provides real-world case studies, insights into deep learning model evaluation, and a glimpse into the future of this field. With practical code examples and exercises, this book empowers readers to assess and enhance their machine learning models. It is a valuable resource for anyone looking to master model evaluation techniques for improved machine learning outcomes.
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