![]() Model_rf_tune_auto <- caret :: train(Class ~. # The final value used for the model was mtry = 1. # Accuracy was used to select the optimal model using the largest value. # Resampling results across tuning parameters: # Resampling: Cross-Validated (10 fold, repeated 10 times) # Pre-processing: scaled (4), centered (4) , data = train_data, method = "rf", preProcess = c( "scale", "center"), trControl = trainControl( method = "repeatedcv", number = 10, repeats = 10, savePredictions = TRUE, verboseIter = FALSE), tuneGrid = grid) model_rf_tune_man # Random Forest # create hyperparameter grid set.seed( 42) grid <- id( mtry = c( 1 : 10)) model_rf_tune_man <- caret :: train(Class ~. ![]() ISI Initial Spread Index from the FWI system: 0 to 18.5īUI Buildup Index from the FWI system: 1.1 to 68Ĭlass : Forest Fire presence (fire) or absence (no fire) Temp Max daily temperature in degrees Celsius: 22 to 42įire Weather Indices FFMC Fine Fuel Moisture Code index from the FWI system: 28.6 to 92.5ĭMC Duff Moisture Code index from the FWI system: 1.1 to 65.9ĭC Drought Code index from the FWI system: 7 to 220.4 Weather Observations Date Day, month (‘june’ to ‘september’) for the year 2012 Here are the variables within the dataset: We are going to use machine learning practices to extract trends from the dataset and attempt to predict whether a forest fire will occur on a particular day given environmental conditions. The dataset we’ll be using is called Bejaia_ForestFires.csv and contains information regarding forest fire conditions in Algeria. We’ll introduce the caret package, a popular R package with machine learning tools. In this tutorial, we’ll explore feature engineering, training and test splitting, and model selecting with random forests. 14.1.1 Opening a Channel to the Database.11.1 ggplot2 with Willow Creek Meteorological Data.5.1 Global Carbon-Dioxide Concentrations.3.1 Important R Programming Definitions.
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