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R의 caret로 배우는 Machine Learning
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업데이트됨 2023. 11.RMachine Learning424 videos88 exercises6,200 XP60,242성과 증명서
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필수 조건
Introduction to Regression in R1
Regression Models: Fitting and Evaluating Their Performance
In the first chapter of this course, you'll fit regression models with
train() and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).2
Classification Models: Fitting and Evaluating Their Performance
In this chapter, you'll fit classification models with
train() and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).3
Tuning Model Parameters to Improve Performance
In this chapter, you will use the
train() function to tweak model parameters through cross-validation and grid search.4
Preprocessing Data
In this chapter, you will practice using
train() to preprocess data before fitting models, improving your ability to making accurate predictions.5
Selecting Models: A Case Study in Churn Prediction
In the final chapter of this course, you'll learn how to use
resamples() to compare multiple models and select (or ensemble) the best one(s).