- Neural Networks with Keras Cookbook
- V Kishore Ayyadevara
- 319字
- 2021-07-02 12:46:31
Getting ready
The strategy that we'll adopt to predict default of a customer is as follows:
- Objective: Assign a high probability to the customers who are more likely to default.
- Measurement criterion: Maximize the number of customers who have actually defaulted when we consider only the top 10% of members by decreasing the default probability.
The strategy we will be adopting to assign a probability of default for each member will be as follows:
- Consider the historic data of all members.
- Understand the variables that can help us to identify a customer who is likely to default:
- Income-to-debt ratio is a very good indicator of whether a member is likely to default.
- We will be extracting a few other variables similar to that.
- In the previous step, we created the input variables; now, let's go ahead and create the dependent variable:
- We will extract the members who have actually defaulted in the next 2 years by first going back in history and then looking at whether members defaulted in the next 2 years
- It is important to have a time lag, as it might not give us any levers to change the outcome if we do not have a time gap between when a member is likely to default and the date of prediction.
- Given that the outcome is binary, we will minimize the binary cross-entropy loss.
- The model shall have a hidden layer that connects the input layer and the output layer.
- We shall calculate the number of the top 10% probability members who have actually defaulted, in the test dataset.
Note that we assume that test data is representative here, as we are not in a position to assess the performance of a model on unseen dataset without productionalizing the model. We shall assume that the model's performance on an unseen dataset is a good indicator of how well the model will perform on future data.
推薦閱讀
- Embedded Linux Projects Using Yocto Project Cookbook
- 軟件項目估算
- Java 9 Concurrency Cookbook(Second Edition)
- Oracle Exadata性能優化
- QGIS:Becoming a GIS Power User
- Python深度學習:模型、方法與實現
- 利用Python進行數據分析
- Hands-On Full Stack Development with Spring Boot 2.0 and React
- Clojure for Machine Learning
- 新印象:解構UI界面設計
- Vue.js 3應用開發與核心源碼解析
- OpenCV 3.0 Computer Vision with Java
- 編程的原則:改善代碼質量的101個方法
- Java程序設計
- C語言從入門到精通(視頻實戰版)