Data Mining Questions et reponses d'entretien
Question 1. What is data mining?
Data mining is the process of discovering patterns, trends, and useful information from large datasets.
Example:
Identifying customer purchasing behavior in an e-commerce dataset.
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Question 2. Explain the difference between supervised and unsupervised learning.
Supervised learning involves training a model on a labeled dataset, while unsupervised learning deals with unlabeled data.
Example:
Supervised: Predicting house prices with labeled training data. Unsupervised: Clustering similar documents without labels.
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Question 3. What is the curse of dimensionality?
The curse of dimensionality refers to the challenges and increased computational complexity that arise when working with high-dimensional data.
Example:
In high-dimensional space, data points become sparser, making it harder to generalize patterns.
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Question 4. Name a popular algorithm for association rule mining.
Apriori algorithm.
Example:
Identifying frequent itemsets in a retail transaction dataset.
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Question 5. What is cross-validation, and why is it important in machine learning?
Cross-validation is a technique to assess how well a model will generalize to an independent dataset. It helps detect overfitting.
Example:
Performing k-fold cross-validation to evaluate a classifier's performance.
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