If the Cumulative Gain at a depth of 10% for the Decision Tree is converted to number of primary/positive event cases, what will be the number of cases? Show your calculation **Churn Prediction Analysis**Assume there are 3,600 cases in the validation dataset, and 12% of these cases have a value of 1 for churn (the primary/positive event). Questions a) to c) are based on data for the 3,600 cases (see table below).| Model | Depth (% Contacted) | Cumulative Gain | Cumulative Lift ||----------------------|---------------------|-----------------|-----------------|| Decision Tree | 5 | 34.42 | 6.84 || Logistic Regression | 5 | 20.19 | 4.01 || Neural Network | 5 | 34.62 | 6.88 || Decision Tree | 10 | 64.90 | 6.06 || Logistic Regression | 10 | 36.06 | 3.15 || Neural Network | 10 | 62.50 | 5.54 || Decision Tree | 15 | 73.96 | 1.82 || Logistic Regression | 15 | 49.04 | 2.62 || Neural Network | 15 | 82.21 | 3.97 || Decision Tree | 20 | 73.89 | 0.87 || Logistic Regression | 20 | 59.13 | 2.01 || Neural Network | 20 | 86.54 | 0.86 |**Explanatory Notes:**- **Cumulative Gain:** This column represents the percentage of positive events (churn) captured when a certain percentage of the dataset is contacted, based on model predictions.- **Cumulative Lift:** This column measures the effectiveness of a model in predicting churn. A lift of 1 would mean the model is no better than random selection. Higher values indicate better performance.The table is structured to compare the performance of three machine learning models (Decision Tree, Logistic Regression, and Neural Network) across different depths or percentages of the dataset contacted. The analysis helps determine which model and depth provide the most effective tool for predicting churn within a dataset.

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7. Assume there are 3,600 cases in the validation dataset, and 12% of these cases have a value of 1 for churn (the primary/positive event). Questions a) to c) are based on data for the 3,600 cases (see table below). Model Decision Tree Logistic Regression Neural Network Decision Tree Logistic Regression Neural Network Decision Tree Logistic Regression Neural Network Decision Tree Logistic Regression Neural Network Depth (% Contacted) 5 5 5 10 10 10 15 15 15 20 20 20 Cumulative Gain Cumulative Lift 34.42 6.84 20.19 4.01 34.62 6.88 64.90 36.06 62.50 73.96 49.04 82.21 78.39 59.13 86.54 6.06 3.15 5.54 1.82 2.62 3.97 0.87 2.01 0.86

7. Assume there are 3,600 cases in the validation dataset, and 12% of these cases have a value of 1 for churn (the primary/positive event). Questions a) to c) are based on data for the 3,600 cases (see table below). Model Decision Tree Logistic Regression Neural Network Decision Tree Logistic Regression Neural Network Decision Tree Logistic Regression Neural Network Decision Tree Logistic Regression Neural Network Depth (% Contacted) 5 5 5 10 10 10 15 15 15 20 20 20 Cumulative Gain Cumulative Lift 34.42 6.84 20.19 4.01 34.62 6.88 64.90 36.06 62.50 73.96 49.04 82.21 78.39 59.13 86.54 6.06 3.15 5.54 1.82 2.62 3.97 0.87 2.01 0.86

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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