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Preparation

Importing the raw dataset

df <- read.csv("d:/ITALY-1/osk-c.csv")
df <- df[,-1]
head(df,5)
##   Event PFS                       Group
## 1     1 143 surgery without Fluorescein
## 2     1 120 surgery without Fluorescein
## 3     1 112 surgery without Fluorescein
## 4     1 131 surgery without Fluorescein
## 5     1 215 surgery without Fluorescein
set.seed(123)
split = sample(nrow(df),nrow(df)*0.75)
training_set = df[split,]
test_set = df[-split,]

Logistic Regression Model

## Warning in train.default(x, y, weights = w, ...): You are trying to do
## regression and your outcome only has two possible values Are you trying to do
## classification? If so, use a 2 level factor as your outcome column.
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
## There were missing values in resampled performance measures.

Explainer

## Preparation of a new explainer is initiated
##   -> model label       :  train.formula  (default)
##   -> data              :  27  rows  2  cols 
##   -> target variable   :  27  values 
##   -> predict function  :  yhat.train  will be used (default)
##   -> predicted values  :  numerical, min =  0.4413983 , mean =  0.9595112 , max =  1  
##   -> residual function :  difference between y and yhat (default)
##   -> residuals         :  numerical, min =  2.294942e-11 , mean =  0.04048878 , max =  0.5586017  
## A new explainer has been created!

1. Model performance

2. Variable importance

## Warning: Please note that 'variable_importance()' is now deprecated, it is better to use 'ingredients::feature_importance()' instead.
## Find examples and detailed introduction at: https://pbiecek.github.io/PM_VEE/featureImportance.html

###3. Partial Depedence Plot

## Warning: Please note that 'variable_response()' is now deprecated, it is better to use 'ingredients::partial_dependency()' instead.
## Find examples and detailed introduction at: https://pbiecek.github.io/PM_VEE/partialDependenceProfiles.html

## Variable Group  is of the class factor. Type of explainer changed to 'factor'.
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.

4.Acumulated Local Effects plot

## Warning: Please note that 'variable_response()' is now deprecated, it is better to use 'ingredients::accumulated_dependency()' instead.
## Find examples and detailed introduction at: https://pbiecek.github.io/PM_VEE/accumulatedLocalProfiles.html