Data Science Questions et reponses d'entretien
Question 21. What is the purpose of the term 'bias-variance tradeoff' in machine learning?
The bias-variance tradeoff represents the balance between underfitting (high bias) and overfitting (high variance) in a machine learning model. Achieving an optimal tradeoff is crucial for model generalization.
Example:
Increasing model complexity may reduce bias but increase variance, leading to overfitting.
Question 22. Explain the term 'one-hot encoding' and its application in machine learning.
One-hot encoding is a technique used to represent categorical variables as binary vectors. Each category is represented by a unique binary digit, and this encoding is valuable when working with algorithms that require numerical input.
Example:
Converting categorical variables like 'color' into binary vectors (e.g., red: [1, 0, 0], blue: [0, 1, 0], green: [0, 0, 1]).
Question 23. What is the purpose of the term 'confusion matrix' in classification?
A confusion matrix is a table that evaluates the performance of a classification model by presenting the counts of true positives, true negatives, false positives, and false negatives. It is useful for assessing model accuracy, precision, recall, and F1 score.
Example:
For a binary classification problem, a confusion matrix might look like: [[TN, FP], [FN, TP]].
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