Classification using Random Forest¶

In [1]:

# DAL ToolBox
# version 1.01.727

source("https://raw.githubusercontent.com/cefet-rj-dal/daltoolbox/main/jupyter.R")

#loading DAL
load_library("daltoolbox")

Loading required package: daltoolbox

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 


Attaching package: ‘daltoolbox’


The following object is masked from ‘package:base’:

    transform

In [2]:

iris <- datasets::iris
head(iris)

A data.frame: 6 × 5
	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	<dbl>	<dbl>	<dbl>	<dbl>	<fct>
1	5.1	3.5	1.4	0.2	setosa
2	4.9	3.0	1.4	0.2	setosa
3	4.7	3.2	1.3	0.2	setosa
4	4.6	3.1	1.5	0.2	setosa
5	5.0	3.6	1.4	0.2	setosa
6	5.4	3.9	1.7	0.4	setosa

In [3]:

#extracting the levels for the dataset
slevels <- levels(iris$Species)
slevels

'setosa'
'versicolor'
'virginica'

Building samples (training and testing)¶

In [4]:

# preparing dataset for random sampling
set.seed(1)
sr <- sample_random()
sr <- train_test(sr, iris)
iris_train <- sr$train
iris_test <- sr$test

In [5]:

tbl <- rbind(table(iris[,"Species"]), 
             table(iris_train[,"Species"]), 
             table(iris_test[,"Species"]))
rownames(tbl) <- c("dataset", "training", "test")
head(tbl)

A matrix: 3 × 3 of type int
	setosa	versicolor	virginica
dataset	50	50	50
training	39	38	43
test	11	12	7

Model training¶

In [6]:

model <- cla_rf("Species", slevels, mtry=3, ntree=5)
model <- fit(model, iris_train)
train_prediction <- predict(model, iris_train)

Model adjustment¶

In [7]:

iris_train_predictand <- adjust_class_label(iris_train[,"Species"])
train_eval <- evaluate(model, iris_train_predictand, train_prediction)
print(train_eval$metrics)

  accuracy TP TN FP FN precision recall sensitivity specificity f1
1    0.975 39 81  0  0         1      1           1           1  1

Model testing¶

In [8]:

# Test
test_prediction <- predict(model, iris_test)

iris_test_predictand <- adjust_class_label(iris_test[,"Species"])

#Avaliação #setosa
test_eval <- evaluate(model, iris_test_predictand, test_prediction)
print(test_eval$metrics)

#Avaliação #versicolor
test_eval <- evaluate(model, iris_test_predictand, test_prediction, ref=2)
print(test_eval$metrics)

#Avaliação #virginica
test_eval <- evaluate(model, iris_test_predictand, test_prediction, ref=3)
print(test_eval$metrics)

   accuracy TP TN FP FN precision recall sensitivity specificity f1
1 0.9666667 11 19  0  0         1      1           1           1  1
   accuracy TP TN FP FN precision recall sensitivity specificity   f1
1 0.9666667 12 17  1  0 0.9230769      1           1   0.9444444 0.96
   accuracy TP TN FP FN precision    recall sensitivity specificity        f1
1 0.9666667  6 23  0  1         1 0.8571429   0.8571429           1 0.9230769

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