CODEWITHSUNDEEP
pythonscikit-learnkdtree
mark
mark

Reputation: 75

Setting k for KDTree search: chatgpt seems wrong

I have dataset consisting of 30 samples and 5 features. I want kdtree search across all 30 samples and 5 features . What should be the value for "k" parameter ?

from sklearn.neighbors import KDTree

# Assuming your data is in a variable called 'data'
tree = KDTree(data)

# Query point
query_point = [1.0, 2.0, 3.0, 4.0, 5.0]

# Find the 5 nearest neighbors
distances, indices = tree.query([query_point], k=5)

# 'indices' will contain the indices of the 5 nearest neighbors
# 'distances' will contain the distances to these neighbors

The chatgpt says that it should be 5. But I do not think so. Do someone know about it?

Upvotes: -1

Views: 86

Answers (1)

Luca Micarelli
Luca Micarelli

Reputation: 129

The value for the "k" parameter in a KDTree search determines how many nearest neighbors you want to find for a given query point. In your code example, you are looking for the 5 nearest neighbors to the query_point.

So, if you want to find the 5 nearest neighbors, setting k=5 is the correct choice in this case. The code you provided will return the indices of the 5 nearest neighbors and their corresponding distances from the query_point.

Upvotes: 2

Related Questions