Caret Machine Learning Cheat Sheet for Training and Evaluation
A Chinese R machine-learning reference covering data splitting, preprocessing, resampling, training, tuning, metrics, leakage checks and held-out evaluation.
What this reference covers
This reference sheet breaks an R machine-learning project into target definition, train-validation-test splitting, preprocessing, resampling, model training, tuning and evaluation. It is a workflow index rather than a model-selection decision; reliable results depend on experiment records and an independent test set.
Keep preprocessing inside the training flow
Missing-value handling, scaling, encoding and feature selection must be fitted within the training data or training folds. If test information enters preprocessing, cross-validation scores can look stronger than the model will perform on new data. Record the split, seed, resampling plan and metric before tuning.
Interpret metrics in context
Accuracy alone can hide class imbalance. Compare precision, recall, threshold behavior, confusion matrix and error cost for classification; for regression, inspect error distribution and meaningful baseline comparisons. After tuning, evaluate the held-out test set once and preserve the model and preprocessing objects.
Maintenance note
Caret interfaces, engines and dependencies may change. This page supports study and experiment planning, not a performance guarantee or production deployment decision. Content review date: 2026-08-23.
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Caret machine-learning workflow study guide
Define the task and metric, split the data, fit preprocessing inside resampling, compare simple baselines and evaluate the held-out set only after tuning.
Before you start
- Prepare a PDF reader, an R environment and a de-identified classification or regression sample.
- Record target variable, data split, metric, acceptable error and random seed.
- Create an experiment log for preprocessing, resampling, parameters, runtime and errors.
Quick start
- 01
Define target and split
Decide classification or regression, set the target and metric, and separate training, validation and held-out test data.
- 02
Build the preprocessing flow
Place missing-value handling, scaling, encoding and feature selection inside a reusable training workflow.
- 03
Train baseline models
Fix seed and resampling, compare a few simple baselines and record parameters, runtime, metrics and warnings.
- 04
Evaluate the held-out set
After tuning, evaluate the test set once and save confusion matrix or error distribution with model and preprocessing versions.
Usage tips
- Check for leakage whenever preprocessing or feature selection is moved across folds.
- Select metrics that reflect class imbalance, threshold choices and the cost of errors.
- Retain the preprocessing object used in training so new data receives the same transformations.
Troubleshooting and uninstall
Why are training metrics high but test metrics poor?
Recheck split, leakage, class distribution, resampling and overfitting, then compare a simple baseline and learning curve.
Why can new data not be scored after preprocessing?
Compare new-data columns, types, factor levels and missing handling with the stored training workflow and preprocessing object.
Frequently asked questions
Which Caret workflow topics are covered?
The sheet covers splitting, preprocessing, resampling, training, tuning, metrics and held-out evaluation in R.
Does it include a trained model or dataset?
No. It is a workflow and function reference; training requires the project's own data and environment.
What is a common source of misleading scores?
Data leakage, an incorrect split, class imbalance, an unsuitable metric or repeated use of the test set can all distort scores.
When should the test set be used?
Keep it untouched during tuning and use it for a final evaluation after the workflow and parameters are fixed.