Data Mining 面接の質問と回答
質問 11. What is the purpose of data preprocessing in data mining?
Data preprocessing involves cleaning and transforming raw data into a format suitable for analysis. It helps improve the quality of results and reduces errors.
Example:
Handling missing values, removing duplicates, and scaling numerical features in a dataset.
質問 12. Explain the concept of overfitting in machine learning.
Overfitting occurs when a model learns the training data too well, capturing noise and irrelevant patterns. As a result, it performs poorly on new, unseen data.
Example:
A decision tree with too many branches that perfectly fit the training data but fails to generalize to new data.
質問 13. What is the role of a decision tree in data mining?
A decision tree is a predictive modeling tool used for classification and regression tasks. It recursively splits data based on features to make decisions.
Example:
Predicting whether a customer will churn based on factors like usage patterns and customer service interactions.
質問 14. What is the K-nearest neighbors (KNN) algorithm?
KNN is a classification and regression algorithm that assigns a new data point's label based on the majority class or average of its K nearest neighbors in the feature space.
Example:
Classifying an unknown flower species based on the characteristics of its K nearest neighbors in a dataset.
質問 15. How does dimensionality reduction help in data mining?
Dimensionality reduction techniques reduce the number of features in a dataset while preserving its essential information. This helps mitigate the curse of dimensionality and improve model performance.
Example:
Applying Principal Component Analysis (PCA) to transform high-dimensional data into a lower-dimensional space.
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