17 Questions
In the K-Nearest Neighbor algorithm, what does the value of K represent?
The number of nearest neighbors considered for classification
What is the main advantage of using a larger value of K in K-Nearest Neighbor?
It reduces the impact of noise and outliers in the training data
What is the primary advantage of the distance-weighted nearest neighbor approach over the standard K-Nearest Neighbor algorithm?
It gives more weight to closer neighbors, potentially improving accuracy
Which of the following statements is true about the K-Nearest Neighbor algorithm?
It is a lazy learning algorithm that does not require explicit training
When might the K-Nearest Neighbor algorithm be a good choice for a machine learning task?
When the target function is highly complex but can be approximated by local simple approximations
What is a potential disadvantage of the K-Nearest Neighbor algorithm?
It requires a large amount of memory to store the training instances
What is the most basic instance-based model described in the text?
K-Nearest Neighbours (KNN)
In the 2-D example given, how many attributes are used to describe each instance?
2
What is the formula used to calculate the Euclidean distance between two instances $x_i$ and $x_j$ with $n$ attributes?
$d(x_i, x_j) = \sqrt{\sum_{r=1}^n (a_r(x_i) - a_r(x_j))^2}$
What is the purpose of standardizing/normalizing the features when calculating Euclidean distance?
To ensure the distance metric is scale-invariant
How does the K-Nearest Neighbour algorithm handle discrete (nominal) features?
Both (b) and (c)
What is the key step in the K-Nearest Neighbour classification algorithm?
Returning the class with the most neighbors as the prediction
What is the main purpose of the K-Nearest Neighbor (K-NN) algorithm?
To find the class with the most votes among the k closest data points
What is the key difference between K-NN for discrete-valued and continuous-valued target functions?
K-NN works well for discrete-valued target functions, but the idea needs to be extended for continuous-valued target functions
What is the first step in the K-NN algorithm for a given query point $x_q$?
Compute the distance between $x_q$ and all the data points
What is the formula used to determine the class of the query point $x_q$ in the K-NN algorithm?
$F(x_q) = \operatorname{mode}(F(x_1), F(x_2), ..., F(x_k))
What is the purpose of the distance-weighted nearest neighbor approach in K-NN?
To assign more weight to the closer neighbors when determining the class of the query point
Test your knowledge on implementing K-Nearest Neighbors algorithm from scratch. This quiz covers topics such as reading datasets, computing similarity, finding neighbors, calculating argmax, and using Scikit-learn for accuracy computation.
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