Contents
How are features selected in the caret your package?
Selecting the right features in your data can mean the difference between mediocre performance with long training times and great performance with short training times. The caret R package provides tools to automatically report on the relevance and importance of attributes in your data and even select the most important features for you.
Which is the most powerful feature of Caret?
As mentioned above, one of the most powerful aspects of the caret package is the consistent modeling syntax. By simply changing the method argument, you can easily cycle between, for example, running a linear model, a gradient boosting machine model and a LASSO model. In total, there are 233 different models available in caret.
How to tune a 5 model in caret package?
5Model Training and Tuning 5.1Model Training and Parameter Tuning 5.2An Example 5.3Basic Parameter Tuning 5.4Notes on Reproducibility 5.5Customizing the Tuning Process 5.5.1Pre-Processing Options 5.5.2Alternate Tuning Grids 5.5.3Plotting the Resampling Profile 5.5.4The trainControlFunction 5.5.5Alternate Performance Metrics
How is the train function used in caret?
The caret package has several functions that attempt to streamline the model building and evaluation process. The train function can be used to evaluate, using resampling, the effect of model tuning parameters on performance
Which is an example of feature elimination in caret?
20Recursive Feature Elimination 20.1Backwards Selection 20.2Resampling and External Validation 20.3Recursive Feature Elimination via caret 20.4An Example
How are filter methods used in the caret package?
caret has wrapper methods based on recursive feature elimination, genetic algorithms, and simulated annealing. Filter methods evaluate the relevance of the predictors outside of the predictive models and subsequently model only the predictors that pass some criterion.
How to measure performance in the caret package?
7.0.17Implicit Feature Selection 7.0.18Kernel Method 7.0.19L1 Regularization 7.0.20L2 Regularization 7.0.21Linear Classifier 7.0.22Linear Regression 7.0.23Logic Regression 7.0.24Logistic Regression 7.0.25Mixture Model 7.0.26Model Tree 7.0.27Multivariate Adaptive Regression Splines 7.0.28Neural Network 7.0.29Oblique Tree 7.0.30Ordinal Outcomes