What does the caret package do?
What does the caret package do?
The caret package (short for Classification And REgression Training) contains functions to streamline the model training process for complex regression and classification problems. caret loads packages as needed and assumes that they are installed. If a modeling package is missing, there is a prompt to install it.
What is R caret?
Caret is a one-stop solution for machine learning in R. The R package caret has a powerful train function that allows you to fit over 230 different models using one syntax. There are over 230 models included in the package including various tree-based models, neural nets, deep learning and much more.
How long does caret package take to install?
the install took about 5 hours for me with 100Mbps internet. it took about 10 minutes.
What is Loocv?
The Leave-One-Out Cross-Validation, or LOOCV, procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not used to train the model.
How do you make a caret symbol?
Creating the ^ symbol on a U.S. keyboard To create the caret symbol using a U.S. keyboard hold down the Shift and press the 6 number key at the top of the keyboard.
What is caret HTML?
A caret (sometimes called a “text cursor”) is an indicator displayed on the screen to indicate where text input will be inserted. The word “caret” differentiates the text insertion point from the mouse cursor. …
What is repeated CV?
Repeated k-fold CV does the same as above but more than once. For example, five repeats of 10-fold CV would give 50 total resamples that are averaged. Note this is not the same as 50-fold CV.
What does train do in R?
train can be used to tune models by picking the complexity parameters that are associated with the optimal resampling statistics. For particular model, a grid of parameters (if any) is created and the model is trained on slightly different data for each candidate combination of tuning parameters.
What is the Loocv issue?
In leave-one-out cross-validation (LOOCV), each of the training sets looks very similar to the others, differing in only one observation. When you want to estimate the test error, you take the average of the errors over the folds. That average has a high variance.
