面试题与答案

The bias-variance tradeoff is a key concept in model selection. High bias leads to underfitting, while high variance leads to overfitting. It's about finding the right balance to achieve optimal model performance.

Bagging (Bootstrap Aggregating) and boosting are ensemble learning techniques. Bagging builds multiple models independently and combines them, while boosting builds models sequentially, giving more weight to misclassified instances.

The curse of dimensionality refers to the challenges and issues that arise when working with high-dimensional data. As the number of features increases, the data becomes sparse, and the computational requirements for training models grow exponentially.

L1 regularization adds the absolute values of the coefficients to the cost function, encouraging sparsity, while L2 regularization adds the squared values, penalizing large coefficients. L1 tends to produce sparse models, while L2 prevents extreme values in the coefficients.

Gradient boosting is an ensemble learning technique that builds a series of weak learners, typically decision trees, in a sequential manner. Each new learner corrects the errors of the previous ones, producing a strong, accurate model.

The learning rate determines the size of the steps taken during the optimization process. It is a hyperparameter that influences the convergence and stability of the optimization algorithm. A too-high learning rate may cause divergence, while a too-low rate may result in slow convergence.

Transfer learning is a technique where a pre-trained model on a large dataset is adapted for a different but related task. It allows leveraging knowledge gained from one domain to improve performance in another, often with smaller amounts of task-specific data.

Kernel functions in SVM enable the algorithm to operate in a higher-dimensional space without explicitly calculating the new feature space. They transform the input data into a higher-dimensional space, making it easier to find a hyperplane that separates different classes.