Artificial Intelligence & Machine Learning

Mid‑Level Test of Core ML Algorithms

Test your knowledge of core machine learning algorithms with eight challenging items.

#machine learning#neural networks#algorithms#feature engineering#model evaluation#clustering#decision trees#gradient descent#unsupervised#supervised learning

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Total Questions

Difficulty Level

medium

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0/0

Quiz Questions

Answer all questions below and test your knowledge.

1
Which algorithm iteratively updates parameters to minimize a differentiable loss using the steepest slope?
Question 1
2
In a decision tree, which impurity measure calculates the probability that a randomly chosen element would be incorrectly labeled if it were randomly labeled according to the distribution of labels in the node?
Question 2
3
Which clustering technique partitions data by assigning points to the nearest of a predetermined number of centroids and then recomputes centroids until convergence?
Question 3
4
Which supervised method constructs a hyperplane that maximizes the margin between two classes in a high‑dimensional feature space?
Question 4
5
Which algorithm builds an ensemble by training successive learners on the residual errors of the previous model?
Question 5
6
Which method estimates the probability of a class by applying Bayes’ theorem assuming feature independence?
Question 6
7
Which neural architecture processes data sequentially, maintaining a hidden state that captures information from previous time steps?
Question 7
8
Which dimensionality reduction technique projects data onto orthogonal axes that capture the greatest variance?
Question 8

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Mid‑Level Test of Core ML Algorithms

Quiz Intelligence

Quiz Questions

Which algorithm iteratively updates parameters to minimize a differentiable loss using the steepest slope?

In a decision tree, which impurity measure calculates the probability that a randomly chosen element would be incorrectly labeled if it were randomly labeled according to the distribution of labels in the node?

Which clustering technique partitions data by assigning points to the nearest of a predetermined number of centroids and then recomputes centroids until convergence?

Which supervised method constructs a hyperplane that maximizes the margin between two classes in a high‑dimensional feature space?

Which algorithm builds an ensemble by training successive learners on the residual errors of the previous model?

Which method estimates the probability of a class by applying Bayes’ theorem assuming feature independence?

Which neural architecture processes data sequentially, maintaining a hidden state that captures information from previous time steps?

Which dimensionality reduction technique projects data onto orthogonal axes that capture the greatest variance?

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