Beginner Explanation
Imagine you’re at a talent show where performers are scored by judges. Each performer gets a score based on how well they did. Now, if you want to find out who the top three performers are, you look at the scores and pick the top three. That’s what top-k rankings do! They help us find the top k items (like performers) based on their scores (or importance) in any situation, like picking the best products to recommend or the most influential factors in a study.Technical Explanation
Top-k rankings are commonly used in machine learning to identify the most relevant features or predictions based on their attribution scores. For example, using frameworks like Scikit-learn or TensorFlow, we can compute the importance of features in a model. In Python, you might use the `feature_importances_` attribute of a tree-based model. Here’s a simple example: “`python from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import load_iris import numpy as np # Load dataset X, y = load_iris(return_X_y=True) # Train model model = RandomForestClassifier() model.fit(X, y) # Get feature importances importances = model.feature_importances_ # Get indices of top-k features k = 2 indices = np.argsort(importances)[-k:][::-1] print(f’Top {k} features: {indices}’) “` This code trains a Random Forest classifier and retrieves the indices of the top k features based on their importance scores.Academic Context
Top-k ranking is a critical concept in various fields, including information retrieval, recommendation systems, and feature selection in machine learning. The mathematical foundation often involves sorting algorithms and optimization techniques. Key papers include ‘Feature Selection: A Data Perspective’ by Liu and Motoda, which discusses methods for selecting the most relevant features. Additionally, the ‘RankSVM’ algorithm proposed by Joachims provides a framework for learning to rank, which is foundational for understanding top-k rankings in machine learning contexts.Code Examples
Example 1:
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
import numpy as np
# Load dataset
X, y = load_iris(return_X_y=True)
# Train model
model = RandomForestClassifier()
model.fit(X, y)
# Get feature importances
importances = model.feature_importances_
# Get indices of top-k features
k = 2
indices = np.argsort(importances)[-k:][::-1]
print(f'Top {k} features: {indices}')Example 2:
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
import numpy as np
# Load datasetExample 3:
from sklearn.datasets import load_iris
import numpy as np
# Load dataset
X, y = load_iris(return_X_y=True)Example 4:
import numpy as np
# Load dataset
X, y = load_iris(return_X_y=True)
View Source: https://arxiv.org/abs/2511.16482v1