![\"Random-Forest-in-R\"](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2017\/12\/Random-Forest-in-R.png\")
<\/a><\/p>\nSo, let’s start.<\/p>\n
Introduction to Random Forest in R<\/h2>\n
What are Random Forests?<\/strong><\/p>\n The idea behind this technique is to decorrelate\u00a0<\/span>the several trees.\u00a0Ensemble technique called Bagging is like random forests. It is generated on the different bootstrapped samples from training data. And, then we reduce the variance in trees by averaging them. Hence, in this approach, it creates a large number of decision trees in R.<\/span> Master the concept of R Decision Trees<\/a>\u00a0before proceeding further<\/strong><\/em><\/p>\n We use the R package “randomForest” to create random forests.<\/span><\/p>\n It is a type of supervised learning technique. The basic idea behind it is to generate many models on a training dataset and then combining their output rules.<\/span><\/p>\n We use it to generate lots of models by training on Training Set and\u00a0combining\u00a0them at the end. Hence, we can use it to improve the predictive performance of decision trees by reducing the variance in the trees through averaging them. It is called the random forest technique.<\/span><\/p>\n It is a type of model which combine results from different models and is usually better than the result from one of the individual models. Some of the features of random forests in R Programming<\/a> are as follows:<\/span><\/p>\n The tutorial to gain expertise in Classification in R Programming<\/a><\/strong><\/em><\/p>\n At training time, we can classify the ensemble learning method of random forest and thus we can operate it by constructing a multitude of decision trees.<\/p>\n Adele Cutler and Leo Breiman developed it. Here the combination of two different methods is done by Leo\u2019s bagging idea<\/strong> and the random selection of features<\/strong> introduced by Tin Kan Ho. He also proposed Random Decision Forest in the year 1995.<\/span><\/p>\n If the number of cases in the training set is N, and the sample N case is at random, each tree will grow. Thus, this sample will be the training set for growing the tree. If there are M input variables, we specify a number m<<M<\/em> such that at each node, m<\/em> variables are selected at random out of the M<\/em>. The value of m is constant during the forest growing and hence, each tree grows to the largest extent possible.<\/p>\n Do you know about Clustering in R Programming Language<\/a><\/strong><\/em><\/p>\n We will first import the important libraries, such as ggplot2<\/em> and randomForest<\/em> as follows:<\/p>\n Output:<\/strong><\/p>\n We will use the popular titanic survival prediction dataset and will import the training and test sets into our two corresponding variables:<\/p>\n Output:<\/strong><\/p>\n You can’t afford to miss the guide on e1071 Package and SVM Training & Testing Models in R<\/a>\u00a0\u00a0<\/strong><\/em><\/p>\n Let us now take a look at the first six entries of our imported training and testing dataset:<\/p>\n Output:<\/strong><\/p>\n Output:<\/strong><\/p>\n Extraction of features is the next most important step towards building our model. We perform this in the form of a function called ‘Feature_extraction’ that will extract features from whatever input data is provided to it whenever it is called.<\/p>\n Code Display:<\/strong><\/p>\n Output:<\/strong><\/p>\n Let us call the above function by displaying the summary of the extracted features of training and test sets:<\/p>\n Output:<\/strong><\/p>\n In the next step, we will implement our randomForest()<\/em> function as follows:<\/p>\n Output:<\/strong><\/p>\n Let us now extract some of the important features with the following lines of code:<\/p>\n Output:<\/strong><\/p>\n In the final step, we will use ggplot2<\/em> to plot our set of important features:<\/p>\n Code Display:<\/strong><\/p>\n Output:<\/strong><\/p>\n
\n<\/i><\/b><\/p>\nWhat is Ensemble Learning in R?<\/h3>\n
What are Ensemble Models in R?<\/h3>\n
\n<\/b><\/p>\n\n
Random Forest Classifier<\/h3>\n
Functions of Random Forest in R<\/h4>\n
Developing Random Forest in R<\/h3>\n
#Author DataFlair\r\ngetwd()\r\nlibrary(ggplot2)\r\nlibrary(randomForest)\r\n<\/pre>\n
<\/a><\/p>\nsetwd(\"\/home\/dataflair\/Titanic Dataset\/\") #DataFlair\r\ntrain_data <- read.csv(\"train.csv\" , stringsAsFactors = FALSE)\r\ntest_data <- read.csv(\"test.csv\", stringsAsFactors = FALSE)\r\n<\/pre>\n
<\/a><\/p>\n> head(train_data)<\/pre>\n
<\/a><\/p>\nhead(test_data)\r\n<\/pre>\n
<\/a><\/p>\nFeature_extraction <- function(data) {\r\n labels <- c(\"Pclass\",\r\n \"Age\",\r\n \"Sex\",\r\n \"Parch\",\r\n \"SibSp\",\r\n \"Fare\",\r\n \"Embarked\")\r\n features <- data[,labels]\r\n features$Age[is.na(features$Age)] <- -1\r\n features$Fare[is.na(features$Fare)] <- median(features$Fare, na.rm=TRUE)\r\n features$Embarked[features$Embarked==\"\"] = \"S\"\r\n features$Sex <- as.factor(features$Sex)\r\n features$Embarked <- as.factor(features$Embarked)\r\n return(features)\r\n}\r\n<\/pre>\n<\/a><\/p>\n<\/a><\/p>\n> summary(Feature_extraction(train_data))\r\n> summary(Feature_extraction(test_data))\r\n<\/pre>\n
<\/a><\/p>\n> r_forest <- randomForest(Feature_extraction(train_data), as.factor(train_data$Survived), ntree=100, importance=TRUE) #Author DataFlair\r\n> r_forest\r\n<\/pre>\n
<\/a><\/p>\n> important <- importance(r_forest, type=1 ) #Author DataFlair\r\n> Important_Features <- data.frame(Feature = row.names(important), Importance = important[, 1])\r\n<\/pre>\n
<\/a><\/p>\n#Author DataFlair\r\nplot_ <- ggplot(Important_Features, \r\n aes(x= reorder(Feature,\r\nImportance) , y = Importance) ) +\r\ngeom_bar(stat = \"identity\", \r\n fill = \"#800080\") +\r\ncoord_flip() +\r\ntheme_light(base_size = 20) +\r\nxlab(\"\") + \r\nylab(\"Importance\")+\r\nggtitle(\"Important Features in Random Forest\\n\") +\r\ntheme(plot.title = element_text(size=18))\r\nggsave(\"important_features.png\", \r\n plot_)\r\nplot_\r\n<\/pre>\n
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