If the Cumulative Gain at a depth of 15% for the Neural Network model is converted to number of primary/positive event cases, what will be the number of cases? Show your calculation.

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  1. If the Cumulative Gain at a depth of 15% for the Neural Network model is converted to number of primary/positive event cases, what will be the number of cases? Show your calculation.
### Text and Table Information for an Educational Website

**Scenario:**
There are 3,600 cases in the validation dataset. In this dataset, 12% of the cases have a value of 1 for churn, which is considered the primary or positive event. The following information is derived from analyzing these 3,600 cases using different models.

**Table Explained:**

The table below presents the performance metrics of three types of models: Decision Tree, Logistic Regression, and Neural Network, at different depths of contact. The 'Depth (% Contacted)' column indicates the percentage of cases contacted or considered, and performance is evaluated in terms of 'Cumulative Gain' and 'Cumulative Lift'.

| 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            |

**Explanations:**

- **Cumulative Gain**: This represents the percentage improvement in capturing the positive event (churn) compared to a random model. Higher values indicate better model performance in identifying the churn cases effectively.

- **Cumulative Lift**: This is the ratio between the percentage of targets in a contacted subset versus a random selection. A higher lift indicates that the model is effective at concentrating
Transcribed Image Text:### Text and Table Information for an Educational Website **Scenario:** There are 3,600 cases in the validation dataset. In this dataset, 12% of the cases have a value of 1 for churn, which is considered the primary or positive event. The following information is derived from analyzing these 3,600 cases using different models. **Table Explained:** The table below presents the performance metrics of three types of models: Decision Tree, Logistic Regression, and Neural Network, at different depths of contact. The 'Depth (% Contacted)' column indicates the percentage of cases contacted or considered, and performance is evaluated in terms of 'Cumulative Gain' and 'Cumulative Lift'. | 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 | **Explanations:** - **Cumulative Gain**: This represents the percentage improvement in capturing the positive event (churn) compared to a random model. Higher values indicate better model performance in identifying the churn cases effectively. - **Cumulative Lift**: This is the ratio between the percentage of targets in a contacted subset versus a random selection. A higher lift indicates that the model is effective at concentrating
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