K-Nearest Neighbors Implementation from Scratch Quiz

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In the K-Nearest Neighbor algorithm, what does the value of K represent?

The number of nearest neighbors considered for classification (correct)

The distance metric used to calculate similarity between instances

The number of attributes in the dataset

The threshold for determining whether an instance is a neighbor or not

What is the main advantage of using a larger value of K in K-Nearest Neighbor?

It improves the computational efficiency of the algorithm

It allows for better handling of irrelevant attributes

It increases the model's sensitivity to local patterns in the data

It reduces the impact of noise and outliers in the training data (correct)

What is the primary advantage of the distance-weighted nearest neighbor approach over the standard K-Nearest Neighbor algorithm?

It eliminates the need to choose a value for K

It automatically handles irrelevant attributes in the dataset

It gives more weight to closer neighbors, potentially improving accuracy (correct)

It reduces the computational complexity of the algorithm

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 Signup and view all the answers

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 Signup and view all the answers

What is a potential disadvantage of the K-Nearest Neighbor algorithm?

It requires a large amount of memory to store the training instances Signup and view all the answers

What is the most basic instance-based model described in the text?

K-Nearest Neighbours (KNN) Signup and view all the answers

In the 2-D example given, how many attributes are used to describe each instance?

2 Signup and view all the answers

What is the formula used to calculate the Euclidean distance between two instances $x_i$ and $x_j$ with $n$ attributes?

$d(x_i, x_j) = \sqrt{\sum_{r=1}^n (a_r(x_i) - a_r(x_j))^2}$ Signup and view all the answers

What is the purpose of standardizing/normalizing the features when calculating Euclidean distance?

To ensure the distance metric is scale-invariant Signup and view all the answers

How does the K-Nearest Neighbour algorithm handle discrete (nominal) features?

Both (b) and (c) Signup and view all the answers

What is the key step in the K-Nearest Neighbour classification algorithm?

Returning the class with the most neighbors as the prediction Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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 Signup and view all the answers

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)) Signup and view all the answers

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 Signup and view all the answers

K-Nearest Neighbors Implementation from Scratch Quiz

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