机器学习训练集之traing 、validation、test data set

本文介绍了监督学习的基本概念,包括所需的数据集类型及其用途。详细解释了训练、验证和测试阶段的作用,并提供了不同阶段数据比例分配的建议。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

Normally to perform supervised learning you need two types of data sets:

  1. In one dataset (your "gold standard") you have the input data together with correct/expected output, This dataset is usually duly prepared either by humans or by collecting some data in semi-automated way. But it is important that you have the expected output for every data row here, because you need for supervised learning.

  2. The data you are going to apply your model to. In many cases this is the data where you are interested for the output of your model and thus you don't have any "expected" output here yet.

While performing machine learning you do the following:

  1. Training phase: you present your data from your "gold standard" and train your model, by pairing the input with expected output.
  2. Validation/Test phase: in order to estimate how well your model has been trained (that is dependent upon the size of your data, the value you would like to predict, input etc) and to estimate model properties (mean error for numeric predictors, classification errors for classifiers, recall and precision for IR-models etc.)
  3. Application phase: now you apply your freshly-developed model to the real-world data and get the results. Since you normally don't have any reference value in this type of data (otherwise, why would you need your model?), you can only speculate about the quality of your model output using the results of your validation phase.

The validation phase is often split into two parts:

  1. In the first part you just look at your models and select the best performing approach using the validation data (=validation)
  2. Then you estimate the accuracy of the selected approach (=test).

Hence the separation to 50/25/25.

In case if you don't need to choose an appropriate model from several rivaling approaches, you can just re-partition your set that you basically have only training set and test set, without performing the validation of your trained model. I personally partition them 70/30 then.

评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值