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A RANDOM FOREST MODEL FOR BREAST CANCER CLASSIFICATION: BUILDING AND OPTIMIZATION OF RANDOM FOREST MODEL FOR CLASSIFICATION OF BREAST CANCER INTO BENIGN AND MALIGNANT - Brossura
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A Random Forest Model for Breast Cancer Classification
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo L2-9786208449407
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A Random Forest Model for Breast Cancer Classification
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LAP Lambert Academic Publishing, 2025
ISBN 10: 6208449405
ISBN 13: 9786208449407
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A Random Forest Model for Breast Cancer Classification
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo L2-9786208449407
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A RANDOM FOREST MODEL FOR BREAST CANCER CLASSIFICATION
Editore:
LAP LAMBERT Academic Publishing Aug 2025, 2025
ISBN 10: 6208449405
ISBN 13: 9786208449407
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Taschenbuch
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 52 pp. Englisch. Codice articolo 9786208449407
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A Random Forest Model for Breast Cancer Classification
Editore:
LAP Lambert Academic Publishing, 2025
ISBN 10: 6208449405
ISBN 13: 9786208449407
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Da: Books Puddle, New York, NY, U.S.A.
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A Random Forest Model for Breast Cancer Classification
Editore:
LAP Lambert Academic Publishing, 2025
ISBN 10: 6208449405
ISBN 13: 9786208449407
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A Random Forest Model for Breast Cancer Classification
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LAP Lambert Academic Publishing, 2025
ISBN 10: 6208449405
ISBN 13: 9786208449407
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A Random Forest Model for Breast Cancer Classification (Paperback)
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LAP Lambert Academic Publishing, 2025
ISBN 10: 6208449405
ISBN 13: 9786208449407
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Paperback. Condizione: new. Paperback. This book highlights on development and optimization of a Random Forest (RF) model to classify breast cancer as benign or malignant using the Wisconsin Breast Cancer Dataset. After preprocessing 569 samples (357 benign, 212 malignant), a default RF model achieved 95.61% accuracy. To improve results, hyperparameter tuning via Grid Search was applied, adjusting parameters such as number of trees (150), max depth (None), min samples split (2), min samples leaf (1), and random seed (123). The optimized RF model achieved 99.12% accuracy, precision, recall, and F1-score, outperforming other methods like SVM, XGBoost, and prior RF implementations. Results show reduced false negatives and no false positives, indicating high sensitivity and specificity. The work underscores the value of meticulous hyperparameter tuning in medical AI applications and suggests future integration with neural networks and hybrid models for enhanced performance in clinical breast cancer diagnosis. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9786208449407
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A RANDOM FOREST MODEL FOR BREAST CANCER CLASSIFICATION
Editore:
LAP LAMBERT Academic Publishing Aug 2025, 2025
ISBN 10: 6208449405
ISBN 13: 9786208449407
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Taschenbuch
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book highlights on development and optimization of a Random Forest (RF) model to classify breast cancer as benign or malignant using the Wisconsin Breast Cancer Dataset. After preprocessing 569 samples (357 benign, 212 malignant), a default RF model achieved 95.61% accuracy. To improve results, hyperparameter tuning via Grid Search was applied, adjusting parameters such as number of trees (150), max depth (None), min samples split (2), min samples leaf (1), and random seed (123). The optimized RF model achieved 99.12% accuracy, precision, recall, and F1-score, outperforming other methods like SVM, XGBoost, and prior RF implementations. Results show reduced false negatives and no false positives, indicating high sensitivity and specificity. The work underscores the value of meticulous hyperparameter tuning in medical AI applications and suggests future integration with neural networks and hybrid models for enhanced performance in clinical breast cancer diagnosis.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch. Codice articolo 9786208449407
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A RANDOM FOREST MODEL FOR BREAST CANCER CLASSIFICATION : BUILDING AND OPTIMIZATION OF RANDOM FOREST MODEL FOR CLASSIFICATION OF BREAST CANCER INTO BENIGN AND MALIGNANT
Editore:
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6208449405
ISBN 13: 9786208449407
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Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book highlights on development and optimization of a Random Forest (RF) model to classify breast cancer as benign or malignant using the Wisconsin Breast Cancer Dataset. After preprocessing 569 samples (357 benign, 212 malignant), a default RF model achieved 95.61% accuracy. To improve results, hyperparameter tuning via Grid Search was applied, adjusting parameters such as number of trees (150), max depth (None), min samples split (2), min samples leaf (1), and random seed (123). The optimized RF model achieved 99.12% accuracy, precision, recall, and F1-score, outperforming other methods like SVM, XGBoost, and prior RF implementations. Results show reduced false negatives and no false positives, indicating high sensitivity and specificity. The work underscores the value of meticulous hyperparameter tuning in medical AI applications and suggests future integration with neural networks and hybrid models for enhanced performance in clinical breast cancer diagnosis. Codice articolo 9786208449407
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