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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Assessment and Deployment | 24-30% | - Deploying models into production - Assessing model performance (metrics, ROC curves, confusion matrices) |
| Topic 2: Building Models | 40-46% | - Model comparison and selection - Supervised model creation (decision trees, ensembles, SVM, neural networks) |
| Topic 3: Data Sources | 30-36% | - Dimensionality reduction and feature engineering - Importing and preparing data - Exploring and modifying data |
In machine learning, what does "overfitting" refer to?
Correct Answer: A 🗳️
What is the primary role of a data warehouse in an organization?
Correct Answer: C 🗳️
When building a recommendation system, what does "collaborative filtering" rely on?
Correct Answer: C 🗳️
Which of the following metrics is commonly used to evaluate the performance of a binary classification model in a machine learning pipeline?
Correct Answer: A 🗳️
Refer to the exhibit below:
Based on the output from the Data Exploration node shown in the exhibit, which variable has the most thin tails (most platykurtic distribution)?
Correct Answer: D 🗳️
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