A Random Forest Model for Breast Cancer Classification - Couverture souple

Gumel, A M ; Adam, A N ; Waziri, I Z

9786208449407: A Random Forest Model for Breast Cancer Classification

Couverture souple

ISBN 10 : 6208449405 ISBN 13 : 9786208449407

Editeur : LAP Lambert Academic Publishing, 2025

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�diteur: LAP Lambert Academic Publishing
Date d'�dition: 2025
Langue: anglais
ISBN 10: 6208449405
ISBN 13: 9786208449407
Reliure: Broch�
Nombre de pages: 52
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A Random Forest Model for Breast Cancer Classification

Gumel, A M; Adam, A N; Waziri, I Z

Edit� par LAP Lambert Academic Publishing, 2025

ISBN 10 : 6208449405 ISBN 13 : 9786208449407

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A Random Forest Model for Breast Cancer Classification

Gumel, A M; Adam, A N; Waziri, I Z

Edit� par LAP LAMBERT Academic Publishing, 2025

ISBN 10 : 6208449405 ISBN 13 : 9786208449407

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A RANDOM FOREST MODEL FOR BREAST CANCER CLASSIFICATION

A M Gumel

Edit� par LAP LAMBERT Academic Publishing Aug 2025, 2025

ISBN 10 : 6208449405 ISBN 13 : 9786208449407

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 52 pp. Englisch. N� de r�f. du vendeur 9786208449407

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A Random Forest Model for Breast Cancer Classification

Gumel, A M; Adam, A N; Waziri, I Z

Edit� par LAP Lambert Academic Publishing, 2025

ISBN 10 : 6208449405 ISBN 13 : 9786208449407

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A Random Forest Model for Breast Cancer Classification

Gumel, A M; Adam, A N; Waziri, I Z

Edit� par LAP Lambert Academic Publishing, 2025

ISBN 10 : 6208449405 ISBN 13 : 9786208449407

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A Random Forest Model for Breast Cancer Classification

Gumel, A M; Adam, A N; Waziri, I Z

Edit� par LAP Lambert Academic Publishing, 2025

ISBN 10 : 6208449405 ISBN 13 : 9786208449407

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A Random Forest Model for Breast Cancer Classification (Paperback)

A.M. Gumel

Edit� par LAP Lambert Academic Publishing, 2025

ISBN 10 : 6208449405 ISBN 13 : 9786208449407

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Paperback. Etat : 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. N� de r�f. du vendeur 9786208449407

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A RANDOM FOREST MODEL FOR BREAST CANCER CLASSIFICATION

A M Gumel

Edit� par LAP LAMBERT Academic Publishing Aug 2025, 2025

ISBN 10 : 6208449405 ISBN 13 : 9786208449407

Neuf Taschenbuch

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Taschenbuch. Etat : 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 Landstra�e 99, 66123 Saarbr�cken 52 pp. Englisch. N� de r�f. du vendeur 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

A M Gumel

Edit� par LAP LAMBERT Academic Publishing, 2025

ISBN 10 : 6208449405 ISBN 13 : 9786208449407

Neuf Taschenbuch

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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 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

A M Gumel (u. a.)

Edit� par LAP LAMBERT Academic Publishing, 2025

ISBN 10 : 6208449405 ISBN 13 : 9786208449407

Neuf Taschenbuch

Vendeur : preigu, Osnabr�ck, Allemagne

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Taschenbuch. Etat : Neu. A RANDOM FOREST MODEL FOR BREAST CANCER CLASSIFICATION | BUILDING AND OPTIMIZATION OF RANDOM FOREST MODEL FOR CLASSIFICATION OF BREAST CANCER INTO BENIGN AND MALIGNANT | A M Gumel (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208449407 | Verantwortliche Person f�r die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. N� de r�f. du vendeur 134022182

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