5-fold cross validation The original dataset is randomly divided into five subsets, each containing an equal number of samples. The model is trained and evaluated five times. In each iteration, one of the folds is held out as the validation set, while the remaining four folds are used for training. The model is trained on the four training folds and then tested on the held-out fold. This process is repeated five times, with each fold serving as the validation set once. The performance metrics, such as accuracy or error rate, are recorded for each iteration. The performance metrics obtained from the five iterations are averaged to provide an overall assessment of the model's performance. 將資料集拆分成五等分,每次訓練時以四等分作為 training dataset,剩下一份作為 validation dataset。訓練時紀錄每次的表現並在最後回報五次平均的表現以作為模型的整體評估。 AUC
6/14/2023姓名:張議隆 學號:F74082125 :::spoiler TOC ::: 15.19 :::info Suppose we have the following requirements for a university database that is used to keep track of students’ transcripts:
6/13/2023姓名:張議隆
6/6/2023Student ID: F74082125 Student Name: 張議隆 See full output and data in hackmd website, full output was too long to be shown here. Some Test Results :::warning When the sample data grows, sample time grows significantly. ::: mu_prior_params_$\mu$
5/31/2023or
By clicking below, you agree to our terms of service.
New to HackMD? Sign up