Practice Supervised Learning – Advanced Algorithms (5) - Supervised Learning – Advanced Algorithms

Practice: Supervised Learning – Advanced Algorithms

Lesson 5Supervised Learning – Advanced Algorithms

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10 questions

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5 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does SVM stand for?
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Think about machine learning algorithms that separate classes.
Question 2.
Name one advantage of using Random Forest over a single decision tree.
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Consider how combining outputs from several models can impact performance.
3Question 3.
What is the main goal of Support Vector Machines?
- To minimize bias
- To find an optimal hyperplane
- To handle categorical data
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Think about hyperplanes in geometry.
4Question 4.
True or False: Ensemble methods can reduce overfitting.
- True
- False
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Consider how using multiple perspectives can help average out biases.
Question 5.
Given an imbalanced dataset with a majority class of 90%, how would you approach training a supervised model to ensure it doesn't favor the dominant class?
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Consider the effects of imbalanced class distributions on model learning.
Question 6.
How would you implement a hybrid model leveraging both traditional ML and deep learning methods for a structured dataset?
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Think about how models can complement each other by using strengths from different approaches.

6 more questions in this section. Enroll free to practise them all.