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您的任务是复制并修改两个ML示例。1.信用/风险评分2.使用k-means聚类的客户细分首先，您需要从Knime下载一本电子书，名为：Practicing Data Science。Knime的Stefan Helfrich非常乐意提供一个代码，这样您就可以免费下载任何电子书：在课堂上，我们将查看模型，因此您还需要下载工作流或在Knime Explorer中的示例中找到它们...
Your assignment will be to copy and then modify two ML examples.
1. Credit/Risk Scoring
2. Customer Segmentation using k-means clustering
To start, you will need to download an e-book from Knime called: Practicing Data Science. Knime's Stefan Helfrich was so kind to provide a code so you can download any e-book for free:
In class, we are going to go over the models so you will also need to download the workflows or find them in the Examples in our Knime Explorer. You will find the locations to download the workflows in the e-book.
We might go through most of this in class first (that is the copy part of the assignment). For homework, you are going to modify each one. 1. For the Credit/Risk model, I want you to swap ML technique from Random Forest to decision tree. You will submit a simple table that shows your AUC and accuracy percent for the two ways that you did the modeling. For example, it should look like this:model type AUC (area under curve in ROC graph) Accuracy (from Scorer Node)
Decision Tree .801 .795
Random Forest .822 .792
You should also write a short statement of which model you would recommend and why you would recommend it. For example, you might say that "Bank should use the Random Forest model to predict risk/score because the area under the curve was higher." Or you could say, "Telco should use the Decision Tree model because the accuracy was higher." Your next sentence should say why you chose either AUC or accuracy to make your decision.
You should also submit your Knime export as a knwf file also with your one-page write-up.
2. For the customer segmentation example, you will use two different values for k (10, 5) which represents the number of different customer segments. After you run both, you will submit a simple table that shows the denormalized means for your cluster_0 values for day_mins and eve_mins. For example, it might look like this:
cluster 0 day mins eve mins
k=5 128 105
k=10 89 101
You should also submit your Knime export as a knwf file K Means Clustering
Here is a video explaining kMeans Clustering:
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