How to implement random forest for regression in spark with R using SparklyR package?

Description

To implement random forest for regression in spark with R using SparklyR package

Functions used :

 spark_connect(master = “local”) – To create a spark connection
 sdf_copy_to(spark_connection,R object,name) – To copy data to spark environment
 sdf_partition(spark_dataframe,partitions with weights,seed) – To partition spark dataframe into multiple groups
 ml_random_forest_regressor(train_data,formula) – To build a random forest model
 ml_predict(ml_model,test_data) – To predict the response for the test data
 ml_regression_evaluator(predict,label_col,prediction_col,metric_name) – To evaluate the metrics(RMSE(default),MSE,R2,MAE)

  • Load the sparklyr package
  • Create a spark connection
  • Copy data to spark environment
  • Split the data for training and testing
  • Build the random forest model
  • Predict using the test data
  • Evaluate the metrics

#Load the sparklyr library
library(sparklyr)
#Create a spark connection
sc #Copy data to spark environment
data_s=sdf_copy_to(sc,read.csv(“/…/servo.csv”),”servo”,overwrite= TRUE)
#Split the data for training and testing
partitions=sdf_partition(data_s,training=0.8,test=0.2,seed=111)
train_data=partitions$training
test_data=partitions$test
#Build the linear regression model
rf_model=ml_random_forest_regressor(x =train_data,Class~.)
summary(rf_model)
#Predict using the test data
prediction = ml_predict(rf_model, test_data)
prediction
#Evaluate the metrics
#Default RMSE(Root Mean Square Error)
cat(“Root Mean Squared Error : “,ml_regression_evaluator(prediction, label_col = “Class”,prediction_col = “prediction”))
cat(“\nMean Squared Error : “,ml_regression_evaluator(prediction, label_col = “Class”,prediction_col = “prediction”,metric_name = “mse”))
cat(“\nR-Squared : “,ml_regression_evaluator(prediction, label_col = “Class”,prediction_col = “prediction”,metric_name = “r2”))
cat(“\nMean Absolute Error : “,ml_regression_evaluator(prediction, label_col = “Class”,prediction_col = “prediction”,metric_name = “mae”))

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