Supervised Learning - Linear Models, K-Nearest Neighbours, Principles, Limitations and Validations

What are the key principles of linear models and K-Nearest Neighbours (KNN)?

They are used for predictive analysis and classification tasks, where linear models establish a linear relationship between features and outcomes, while KNN classifies data points based on the closest K training samples.

What is the significance of distributions and Bayes error rate in model evaluation?

Distributions help understand the underlying data structure, while Bayes error rate indicates the lowest possible error rate for a given classification problem, influencing model performance expectations.

What challenges do high-dimensional spaces pose in machine learning?

They can lead to overfitting, increased computational complexity, and difficulty in visualizing and interpreting data.

How can one distinguish between overfitting and generalisation in model performance?

Overfitting refers to a model performing well on training data but poorly on unseen data, while generalisation is the model's ability to perform well on new, unseen data.

Why is feature selection important for model accuracy?

Choosing the right features enhances predictive power and avoids introducing noise that can lead to lower model performance.

What is the role of cross-validation in model evaluation?

Cross-validation assesses model robustness and generalizability, reducing overfitting and ensuring that the model maintains performance on unseen data.

What metrics are important for monitoring model performance?

Accuracy, precision, recall, and F1-score provide a comprehensive evaluation of model effectiveness across various datasets.

What is the benefit of implementing grid search for hyperparameter tuning?

Grid search systematically explores the parameter space to identify optimal settings, enhancing model performance.

How do ensemble methods contribute to predictive robustness?

They combine the strengths of multiple models to reduce variance and improve overall accuracy.

Why is clear documentation of the modelling process vital?

It fosters reproducibility and trust in model results, enabling stakeholders to understand the rationale behind predictions.

What continuous evaluation strategies should be employed in model management?

Regular monitoring, retraining with new data, and updating models in response to changes in data distribution or patterns ensure sustained accuracy and relevance.