每日一练 | Data Scientist & Business Analyst & Leetcode 面试题 312

Resampling methods involve:

1. Repeatedly drawing a sample from the training data;

2. Refitting the model of interest with each new sample;

3. Examining all the refitted models and then drawing conclusions.

There are two major resampling techniques: cross-validation and bootstrapping, both are easy to implement and and broadly applicable. Cross-validation is used for model assessment and model selection, while bootstrapping is most commonly used to measure the accuracy of a parameter estimate of a given learning model.

Resampling methods are useful because they can address the following drawbacks of traditional validation-test approach:

• data are often scarce and we cannot afford to set aside a validation or test set when training a model;

• the model performance on the validation data is highly dependent on how we split the data, and validation error tends to overestimate the test error rate.