Data Science 면접 질문과 답변
Ques 11. Explain the ROC curve and its significance in binary classification.
The Receiver Operating Characteristic (ROC) curve is a graphical representation of a classifier's performance across various threshold settings. It plots the true positive rate against the false positive rate, helping to assess a model's trade-off between sensitivity and specificity.
Example:
A model with a higher Area Under the ROC Curve (AUC-ROC) is generally considered better at distinguishing between classes.
Ques 12. What is the purpose of the term 'p-value' in statistics?
The p-value is a measure that helps assess the evidence against a null hypothesis. In statistical hypothesis testing, a low p-value suggests that the observed data is unlikely under the null hypothesis, leading to its rejection.
Example:
If the p-value is 0.05, there is a 5% chance of observing the data if the null hypothesis is true.
Ques 13. Explain the concept of ensemble learning and give an example.
Ensemble learning combines predictions from multiple models to improve overall performance. Random Forest is an example of an ensemble learning algorithm, which aggregates predictions from multiple decision trees.
Example:
A Random Forest model combining predictions from 100 decision trees to enhance accuracy and reduce overfitting.
Ques 14. Explain the concept of bagging in the context of machine learning.
Bagging (Bootstrap Aggregating) is an ensemble technique where multiple models are trained on random subsets of the training data with replacement. The final prediction is obtained by averaging or voting on individual predictions.
Example:
A Bagged decision tree ensemble, where each tree is trained on a different bootstrap sample of the data.
Ques 15. What is the purpose of the term 'precision' in binary classification?
Precision is a metric that measures the accuracy of positive predictions made by a model. It is the ratio of true positive predictions to the sum of true positives and false positives.
Example:
In fraud detection, precision is crucial to minimize the number of false positives, i.e., legitimate transactions flagged as fraudulent.
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