Data Mining 面接の質問と回答
質問 6. Explain the concept of feature selection.
Feature selection involves choosing the most relevant features to improve model performance and reduce overfitting.
Example:
Selecting key variables for predicting disease outcomes in a healthcare dataset.
役に立ちましたか？
コメントを追加
コメントを見る
質問 7. What is the difference between classification and regression?
Classification predicts categorical outcomes, while regression predicts continuous numerical outcomes.
Example:
Classification: Spam or non-spam email. Regression: Predicting house prices.
役に立ちましたか？
コメントを追加
コメントを見る
質問 8. What is outlier detection, and why is it important?
Outlier detection identifies data points that deviate significantly from the norm. It is crucial for detecting errors or anomalies in datasets.
Example:
Identifying fraudulent transactions in a credit card dataset.
役に立ちましたか？
コメントを追加
コメントを見る
質問 9. Explain the concept of precision and recall in the context of classification.
Precision is the ratio of true positive predictions to the total predicted positives, while recall is the ratio of true positives to the total actual positives.
Example:
Precision: 90% of predicted spam emails were actually spam. Recall: 80% of actual spam emails were correctly predicted.
役に立ちましたか？
コメントを追加
コメントを見る
質問 10. What is the Apriori principle in association rule mining?
The Apriori principle states that if an itemset is frequent, then all of its subsets must also be frequent.
Example:
If {bread, milk} is a frequent itemset, then {bread} and {milk} must also be frequent.
役に立ちましたか？
コメントを追加
コメントを見る
ユーザー評価で最も役立つ内容: