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What is the essence of the K-Nearest Neighbors (KNN) algorithm in machine learning?
Which classification method does not rely on assumptions about data or fit a model using a specific formula?
What concept in KNN helps measure the ‘distance’ or difference between data points?
Which distance metric in KNN is named after the ancient Greek mathematician Euclid?
What does the ‘K’ in K-Nearest Neighbors (KNN) represent?
What is overfitting in the context of KNN?
Why is feature scaling important in K-Nearest Neighbors (KNN)?
What is a real-world example where KNN can be used?
Which dataset is used in the example to demonstrate the K-Nearest Neighbors (KNN) Classifier?
What does the confusion matrix in KNN tell us?
Which metric in the classification report tells us about the accuracy of positive predictions?
What is a limitation of K-Nearest Neighbors (KNN) Classifier?
Which classification model is like the diligent student who works well when there’s a clear linear boundary separating the classes?
What superhero is used as an analogy to describe the K-Nearest Neighbors (KNN) Classifier?
Why is K-Nearest Neighbors (KNN) considered a ‘lazy learner’?
What is the key advantage of K-Nearest Neighbors (KNN) Classifier?
Which distance metric in KNN is named after the grid-like layout of Manhattan’s streets?
What does the ‘K’ parameter in K-Nearest Neighbors (KNN) represent?
Which metric in the classification report tells us how well our model is performing?
What is a limitation of K-Nearest Neighbors (KNN) Classifier when working with large datasets?
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