Question 1

What is feature engineering, and why is it important in predictive modelling?

Accepted Answer

Feature engineering is the process of transforming raw data to enhance model performance. It is crucial for improving accuracy, reducing noise, and making models more interpretable.

Question 2

What role does cross-validation play in predictive modelling?

Accepted Answer

Cross-validation assesses a model's performance across multiple subsets of data, reducing overfitting risks and providing a more reliable estimate of its ability to generalise.

Question 3

How does hyperparameter tuning impact model optimization?

Accepted Answer

Hyperparameter tuning involves adjusting external configurations to optimise model performance. It's crucial for finding the right balance between overfitting and underfitting.

Question 4

What distinguishes ensemble learning in predictive modelling?

Accepted Answer

Ensemble learning combines predictions from multiple models to enhance accuracy and robustness. Methods like bagging (e.g., Random Forest) and boosting improve overall model performance.

Question 5

Why is model interpretability essential in data science?

Accepted Answer

Model interpretability techniques, like feature importance and partial dependence plots, provide insights into a model's decision-making process, enhancing trust and understanding.

Question 6

What is the significance of precision and recall in classification metrics?

Accepted Answer

Precision measures the accuracy of positive predictions, while recall gauges the model's ability to capture all relevant instances. Balancing these metrics is crucial based on the problem context.

Question 7

How does LIME contribute to model interpretability?

Accepted Answer

LIME (Local Interpretable Model-agnostic Explanations) generates locally faithful explanations for individual predictions, aiding in understanding complex models.

Question 8

Can you explain the concept of bootstrapping in bagging methods?

Accepted Answer

Bootstrapping involves creating multiple subsets of the training data by randomly sampling with replacement. In bagging, each subset is used to train a different model, contributing to ensemble learning.

Question 9

Why is balancing model accuracy and interpretability crucial?

Accepted Answer

Striking the right balance ensures that a model is both accurate and understandable, catering to the needs of stakeholders and facilitating trust in the model's predictions.

Question 10

What challenges arise in tuning hyperparameters for predictive models?

Accepted Answer

Striking a balance between overfitting and underfitting can be challenging. Careful consideration of the model complexity and data characteristics is crucial in this process.

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