For this question, use the Wisconsin Breast Cancer Dataset (WBCD) to build a logistic regression (LR)...

 

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For this question, use the Wisconsin Breast Cancer Dataset (WBCD) to build a logistic regression (LR) model. use the entire data set to build the model (no need to split into training and test sets). However for the predictive features use ONLY the following variables: "radius_mean","texture_mean","perimeter_mean","area_mean","smoothness_mean","compactness_mean" "concavity_mean" Once you have built the model, make sure you follow through with the usual LR model activities (print variable coefficients BETA(i), the constant term BETA(0), Odds ratios for variables, the accuracy of the model overall, the ability to predict for a NEW feature vector X New and so on..). Answer the following question with respect to the Jupyter notebook you have built. - What is the predicted PROBABILITY of Malignancy for a patient with a feature vector as below? X_new1-[[17.99,10.38, 122.80, 1001.0, 0.11840 ,0.27760, 0.3001 ]] For this question, use the Wisconsin Breast Cancer Dataset (WBCD) to build a logistic regression (LR) model. use the entire data set to build the model (no need to split into training and test sets). However for the predictive features use ONLY the following variables: "radius_mean","texture_mean","perimeter_mean","area_mean","smoothness_mean","compactness_mean" "concavity_mean" Once you have built the model, make sure you follow through with the usual LR model activities (print variable coefficients BETA(i), the constant term BETA(0), Odds ratios for variables, the accuracy of the model overall, the ability to predict for a NEW feature vector X New and so on..). Answer the following question with respect to the Jupyter notebook you have built. - What is the predicted PROBABILITY of Malignancy for a patient with a feature vector as below? X_new1-[[17.99,10.38, 122.80, 1001.0, 0.11840 ,0.27760, 0.3001 ]]

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To calculate the predicted probability of malignancy for a patient with the feature vector Xnew11799 ... View the full answer