Tool series: TimeGPT_(9) model cross-validation - Code World

Tool series: TimeGPT_(9) model cross-validation

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Origin blog.csdn.net/wjjc1017/article/details/135245065

Tool series: TimeGPT_(9) model cross-validation

Tool series: TimeGPT_(4) prediction interval data

Tool series: TimeGPT_(1) How to obtain token and initial use

Tool series: TimeGPT_(7) Historical data prediction

Tool series: TimeGPT_(3) Handling holidays and special dates

Model cross-validation concept and code summary

Using cross-validation (Cross Validation) for model assessment

Tool series: TimeGPT_(2) Time series forecasting using exogenous variables

Tool series: TimeGPT_(8) Using irregular timestamps for time series prediction

Tool series: TimeGPT_(6) Predict multiple time series at the same time

Notes - Evaluation Model: K-fold cross-validation

Lasso model selection: Cross-Validation / AIC / BIC

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Li Hongyi machine learning assignment 3 - Image Classification, model integration, cross-validation, TTA

TimeGPT: Time Series Forecasting Model Example

[Bayesian model] BO-KNN Bayesian optimized nearest neighbor classification prediction (including K-fold cross-validation accuracy)

Cross-validation points

Cross-validation ideas

YOLOv8 Tutorial Series: 3. K-fold cross-validation - make the best use of every piece of your labeled data (yolov8 target detection + k-fold cross-validation method)

Keras model for cross validation

R language uses the train function of the caret package to build a naive Bayesian model (NB) model to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a KNN proximity algorithm model (KNN) model to build a classification model, trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build an xgboost model (based on the gbtree algorithm) model to build a classification model, trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

"Statistical Learning Methods" (Li Hang): model evaluation selection, regularization and cross-validation, generalization ability, generative model and discriminative model, supervised learning application

R language uses the train function of the caret package to build a conditional inference tree model algorithm (ctree) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a penalty discriminant analysis model (pda) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

Cross-validation (Cross Validation) principle Summary

What is cross-validation (Cross Validation)?

[Machine Learning] Cross-validation (Cross Validation)

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