In this article, we will show you how to make predictions in R with different machine learning models. We will cover a variety of models, including decision tree, random forests, logistic regression, support vector machines, and gradient boosting trees.
The following is a list of predict functions for machine learning models in R. These functions generate predicted probabilities based on validation data.
predict Function: Decision Tree
The predict() function in rpart package is used to generate predictions from the previously built decision tree model on the validation dataset.
library(rpart) tree.pred = predict(tree.model,validation_data, type="prob")
tree.model
: This is the trained decision tree model.validation_data
: This is the validation dataset on which you want to make predictions.type="prob"
: It specifies that you want to obtain the probabilities as the output instead of just the class labels.
predict Function: Random Forest
The predict() function in randomForest package is used to generate predictions from the previously built random forest model on the validation dataset.
library(randomForest) rf.pred = predict(rf.model,validation_data, type="prob")
rf.model
: This is the trained random forest model.validation_data
: This is the validation dataset on which you want to make predictions.type="prob"
: It specifies that you want to obtain the probabilities as the output instead of just the class labels.
predict Function: Logistic Regression
The predict() function is used to generate predictions from the previously built logistic regression model on the validation dataset.
log.pred = predict(log.model,validation_data, type="response")
log.model
: This is the trained logistic regression model.validation_data
: This is the validation dataset on which you want to make predictions.type="response"
: It specifies that you want to obtain the probabilities as the output.
predict Function: Support Vector Machine
The predict() function in e1071 package is used to generate predictions from the previously built support vector machine model on the validation dataset.
library(e1071) svm.pred = predict(svm.model, validation_data, probability = TRUE)
svm.model
: This is the trained support Vector Machine model.validation_data
: This is the validation dataset on which you want to make predictions.probability = TRUE
: It means that you want to see the predicted probabilities as the output.
predict Function: Conditional Inference Tree / Forest
The predict() function in party package is used to generate predictions from the previously built conditional inference tree model on the validation dataset.
library(party) ct.pred = predict(ct.model,validation_data)
ct.model
: This is the trained Conditional Inference Tree Model.validation_data
: This is the validation dataset on which you want to make predictions.
predict Function: Gradient Boosting Tree
The predict() function in gbm package is used to generate predictions from the previously built gradient boosting tree model on the validation dataset.
library(gbm) gbm.pred = predict(gbm.model, newdata=validation_data, type = "response", n.trees=500)
gbm.model
: This is the trained gradient boosting tree model.validation_data
: This is the validation dataset on which you want to make predictions.type="response"
: It specifies that you want to obtain the probabilities as the output.n.trees=500
: Number of Trees.
In Caret Package, there is only one predict function i.e. predict(log.mod,val, type="prob")
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