Machine Learning / AI

What is the difference between L1 and L2 regularization in linear models?

What is the complexity of analytical solutions for linear regression and when is it unstable?

What is PCA (Principal Component Analysis)?

Explain the difference between a decision tree and a random forest.

On an imbalanced dataset, the model has recall 100% and precision 1%. What happened?

What is the Gini criterion and how is it used in building decision trees?

What happens if the classification threshold is set too low?

Can you explain how the F1-score is calculated using precision and recall?

Describe how the Support Vector Machine (SVM) method works and its relation to linear models.

What loss function is used in linear regression?

How does the depth of a tree affect the model's quality and its generalization ability?

Can you explain in more detail what are precision and recall and how they are related to F1-score?

What is an ROC curve and how is it used to evaluate classifiers?

How can the performance of a linear model be improved when working with categorical features?

Why scale features and what scaling methods exist?

Как метрика F1-score помогает в задачах с несбалансированными классами?

What are recall and precision?

Why can a model with ROC-AUC 0.2 achieve ROC-AUC 0.8?

What does the speed model translate into a class label?

Can you explain how the Gini criterion is calculated and how it influences the choice of split in a decision tree?