Predicting Breast Cancer Survivability Using Data Mining Techniques: Comparing between three modeling tools which are : Artificial neural network, decision trees and logistic regression
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Predicting Breast Cancer Survivability Using Data Mining Techniques (2011)
DE PB NWISBN: 9783846538784 bzw. 3846538787, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Schweiz, Versandfertig innert 3 - 5 Werktagen.
Comparing between three modeling tools which are : Artificial neural network, decision trees and logistic regression, This book concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods, and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we found the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques. Taschenbuch, 09.11.2011.
Comparing between three modeling tools which are : Artificial neural network, decision trees and logistic regression, This book concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods, and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we found the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques. Taschenbuch, 09.11.2011.
2
Symbolbild
Predicting Breast Cancer Survivability Using Data Mining Techniques
EN PB NWISBN: 9783846538784 bzw. 3846538787, in Englisch, LAP Lambert Academic Publishing, Taschenbuch, neu.
This book concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods, and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we found the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques.
3
Predicting Breast Cancer Survivability Using Data Mining Techniques: Comparing between three modeling tools which are : Artificial neural network, decision trees and logistic regression (2011)
EN PB NWISBN: 9783846538784 bzw. 3846538787, in Englisch, 136 Seiten, LAP LAMBERT Academic Publishing, Taschenbuch, neu.
Lieferung aus: Vereinigte Staaten von Amerika, Usually ships in 24 hours.
Von Händler/Antiquariat, Amazon.com.
This book concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods, and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we found the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques. Paperback, Label: LAP LAMBERT Academic Publishing, LAP LAMBERT Academic Publishing, Product group: Book, Published: 2011-11-09, Studio: LAP LAMBERT Academic Publishing, Sales rank: 12273465.
Von Händler/Antiquariat, Amazon.com.
This book concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods, and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we found the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques. Paperback, Label: LAP LAMBERT Academic Publishing, LAP LAMBERT Academic Publishing, Product group: Book, Published: 2011-11-09, Studio: LAP LAMBERT Academic Publishing, Sales rank: 12273465.
4
Symbolbild
Predicting Breast Cancer Survivability Using Data Mining Techniques
DE PB NWISBN: 9783846538784 bzw. 3846538787, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Schweiz, 09.11.2011.
Comparing between three modeling tools which are : Artificial neural network, decision trees and logistic regression, This book concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods, and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we found the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques.
Comparing between three modeling tools which are : Artificial neural network, decision trees and logistic regression, This book concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods, and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we found the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques.
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Predicting Breast Cancer Survivability Using Data Mining Techniques: Comparin.
DE NWISBN: 9783846538784 bzw. 3846538787, in Deutsch, neu.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Delivery type: Free, Delivery: Vereinigtes Königreich Großbritannien und Nordirland, Offer location: United Kingdom, Free shipping.
Von Händler/Antiquariat, 1771cohen - bennys bookstore.
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Von Händler/Antiquariat, 1771cohen - bennys bookstore.
Fixed price.
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