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Question 236 - Professional Machine Learning Engineer discussion

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You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?

A.
Build a random forest regression model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance's after the model is trained.
Answers
A.
Build a random forest regression model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance's after the model is trained.
B.
Build an AutoML tabular regression model Configure the model to generate explanations when it makes predictions.
Answers
B.
Build an AutoML tabular regression model Configure the model to generate explanations when it makes predictions.
C.
Build a custom TensorFlow neural network by using Vertex Al custom training Configure the model to generate explanations when it makes predictions.
Answers
C.
Build a custom TensorFlow neural network by using Vertex Al custom training Configure the model to generate explanations when it makes predictions.
D.
Build a random forest classification model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance's after the model is trained.
Answers
D.
Build a random forest classification model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance's after the model is trained.
Suggested answer: D

Explanation:

A random forest is an ensemble learning method that consists of many decision trees. It can be used for both regression and classification tasks. A random forest classification model can predict the probability of churn for each customer by assigning them to different classes, such as high-risk, medium-risk, or low-risk. A random forest model can also generate feature importances, which measure how much each feature contributes to the prediction. Feature importances can help interpret the model and understand what factors influence customer churn. Vertex AI Workbench is an integrated development environment (IDE) that allows you to create and run Jupyter notebooks on Google Cloud. You can use Vertex AI Workbench to build a random forest classification model in Python, using libraries such as scikit-learn or TensorFlow. You can also configure the model to generate feature importances after the model is trained, and visualize them using plots or tables. This solution can help you build an interpretable model for customer churn prediction, and use the results to design marketing campaigns that target at-risk customers.Reference:

Random Forests | scikit-learn

Vertex AI Workbench | Google Cloud

Interpreting Random Forests | Towards Data Science

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asked 18/09/2024
javier mungaray
34 questions
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