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inst/tutorials/C01La_confusion/C01La_confusion.Rmd

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@@ -4,7 +4,7 @@ author: "Guyliann Engels & Philippe Grosjean"
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description: "**SDD III** Exercices sur les matrices de confusion."
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tutorial:
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id: "C01La_confusion"
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version: 1.0.0/4
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version: 1.1.0/4
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output:
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learnr::tutorial:
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progressive: true
@@ -37,9 +37,9 @@ BioDataScience3::learnr_server(input, output, session)
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```{r, echo=FALSE, message=FALSE}
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data.frame(
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"A" = c( 25, 0, 1),
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"B" = c(0, 16, 9),
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"C" = c(0,8, 18),
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"A" = c(250, 0, 10),
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"B" = c(0, 160, 90),
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"C" = c(0, 80, 180),
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row.names = c("A", "B", "C")) %>.%
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knitr::kable(., caption = "Matrice de confusion dont les colonnes représentent la classification par ordinateur et les lignes la classification manuelle.")
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```
@@ -57,10 +57,10 @@ conf
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```
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```{r conf1_h2-hint-1}
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tp <- 16
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fp <- 9
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fn <- 8
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tn <- 44
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tp <- 160
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fp <- 90
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fn <- 80
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tn <- 440
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# calcul de la matrice
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conf <- ___
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conf
@@ -69,28 +69,28 @@ conf
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```{r conf1_h2-solution}
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## Solution ##
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tp <- 16
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fp <- 9
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fn <- 8
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tn <- 44
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tp <- 160
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fp <- 90
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fn <- 80
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tn <- 440
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# calcul de la matrice
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conf <- (tp+tn)/(tp+fp+fn+tn)
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conf
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```
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```{r conf1_h2-check}
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grade_result(
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pass_if(~ identical(.result, ((16+44)/(16+9+8+44))), "Bien joué !")
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pass_if(~ identical(.result, ((160+440)/(160+90+80+440))), "Bien joué !")
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)
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```
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### La taux de vrai positif
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```{r, echo=FALSE, message=FALSE}
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data.frame(
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"A" = c( 9, 4, 1),
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"B" = c(0, 6, 9),
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"C" = c(0,1, 14),
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"A" = c(90, 40, 10),
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"B" = c(0, 60, 90),
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"C" = c(0, 10, 140),
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row.names = c("A", "B", "C")) %>.%
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knitr::kable(., caption = "Matrice de confusion dont les colonnes représentent la classification par ordinateur et les lignes la classification manuelle.")
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```
@@ -108,10 +108,10 @@ conf
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```
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```{r conf2_h2-hint-1}
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tp <- 14
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fp <- 1
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fn <- 10
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tn <- 19
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tp <- 140
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fp <- 10
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fn <- 100
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tn <- 190
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# calcul de la matrice
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conf <- ___
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conf
@@ -120,28 +120,28 @@ conf
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```{r conf2_h2-solution}
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## Solution ##
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tp <- 14
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fp <- 1
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fn <- 10
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tn <- 19
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tp <- 140
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fp <- 10
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fn <- 100
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tn <- 190
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# calcul de la matrice
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conf <- (tp)/(tp+fn)
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conf
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```
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```{r conf2_h2-check}
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grade_result(
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pass_if(~ identical(.result, (14/(14 + 10))), "Bien joué !")
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pass_if(~ identical(.result, (140/(140 + 100))), "Bien joué !")
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)
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```
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### La spécificité
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```{r, echo=FALSE, message=FALSE}
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data.frame(
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"A" = c( 9, 4, 1),
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"B" = c(0, 6, 9),
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"C" = c(0,1, 14),
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"A" = c(90, 40, 10),
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"B" = c(0, 60, 90),
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"C" = c(0, 10, 140),
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row.names = c("A", "B", "C")) %>.%
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knitr::kable(., caption = "Matrice de confusion dont les colonnes représentent la classification par ordinateur et les lignes la classification manuelle.")
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```
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```
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```{r conf3_h2-hint-1}
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tp <- 9
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fp <- 5
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tp <- 90
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fp <- 50
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fn <- 0
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tn <- 30
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tn <- 300
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# calcul de la matrice
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conf <- ___
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conf
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```{r conf3_h2-solution}
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## Solution ##
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tp <- 9
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fp <- 5
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tp <- 90
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fp <- 50
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fn <- 0
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tn <- 30
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tn <- 300
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# calcul de la matrice
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conf <- (tn)/(tn+fp)
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conf
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```
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```{r conf3_h2-check}
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grade_result(
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pass_if(~ identical(.result, (30/(30 + 5))), "Bien joué !")
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pass_if(~ identical(.result, (300/(300 + 50))), "Bien joué !")
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)
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```
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### La précision
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```{r, echo=FALSE, message=FALSE}
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data.frame(
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"A" = c(10, 3, 2),
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"B" = c(0, 0, 15),
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"C" = c(3, 1, 11),
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"A" = c(100, 30, 20),
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"B" = c(0, 10, 150),
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"C" = c(30, 20, 110),
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row.names = c("A", "B", "C")) %>.%
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knitr::kable(., caption = "Matrice de confusion dont les colonnes représentent la classification par ordinateur et les lignes la classification manuelle.")
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```
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```
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```{r conf4_h2-hint-1}
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tp <- 15
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fp <- 0
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fn <- 13
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tn <- 17
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tp <- 10
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fp <- 150
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fn <- 30
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tn <- 260
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# calcul de la matrice
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conf <- ___
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conf
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```{r conf4_h2-solution}
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## Solution ##
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tp <- 15
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fp <- 0
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fn <- 13
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tn <- 17
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tp <- 10
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fp <- 150
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fn <- 30
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tn <- 260
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# calcul de la matrice
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conf <- (tp)/(tp+fp)
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conf
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```
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```{r conf4_h2-check}
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grade_result(
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pass_if(~ identical(.result, (15/(15+0))), "Bien joué !")
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pass_if(~ identical(.result, (10/(10+150))), "Bien joué !")
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)
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```
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