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 - Code World

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

Enterprise 2023-06-24 20:33:40 views: null

NoSuchKey

Guess you like

Origin blog.csdn.net/zhongkeyuanchongqing/article/details/130916020

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

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 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 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 penalty discriminant analysis model (pda) 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 multi-layer perceptron algorithm model (MLP) 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 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

R language uses the train function of the caret package to build a regularized generalized linear model (glmnet) 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 regularized generalized linear model (glmnet) 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

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

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

R language uses the train function of the caret package to build a Bagging AdaBoost model (Bagged AdaBoost) 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 Bagging AdaBoost model (Bagged AdaBoost) 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

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

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

R language uses the lift_curve function of the yardstick package to evaluate the performance of the multiclass (Multiclass) model and view the lift curve (lift curve) of the model on each classification of the multiclass.

Evaluation classification model and model selection and tuning

Image classification - use pytorch to build an AlexNet network model to train your own data set (cat and dog classification)

Model Evaluation and loss of function

pyspark uses XGboost to train model examples

Evaluate the classification model—Confusion Matrix and evaluation indicators

CLASSIFICATION language model R

Notes - Evaluation Model: K-fold cross-validation

R language to build RFM model to understand~~~

R language to build RFM model to understand~~~

Python function parameters matching model (on)

The usage of the least angle regression LARS algorithm package and the selection of model parameters (R language)

keras - to build and train a model-- super Basics

Recommended

Ranking

SpringBoot entry and the advantages and disadvantages

idea maven report system omitted for duplicate solutions

StackOverflow error when casting to a superclass

2019-06-06 Elastic products Compatibility

springcloud gateway集成oauth2.0

HTTP Headers的Request Headers

js declares arrays and adds object variables to arrays

Nginx summary (c) port-based virtual host configuration

6 Best Practices for Contract Management

Codeforces Round #631 (Div. 2)

Daily

More

2025-03-23(0)

2025-03-22(0)

2025-03-21(0)

2025-03-20(0)

2025-03-19(0)

2025-03-18(0)

2025-03-17(0)

2025-03-16(0)

2025-03-15(0)

2025-03-14(0)