Wie man Verwirrungsmatrix in R macht
library(caret)
confusionMatrix(data = your_prediction,
reference = data$y)
ngamy
library(caret)
confusionMatrix(data = your_prediction,
reference = data$y) # Import confusion matrix
from sklearn.metrics import confusion_matrix, classification_report
# Fit the model to the training data
# Predict the labels of the test data: y_pred
# Generate the confusion matrix and classification report
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))import seaborn as sns
from sklearn.metrics import confusion_matrix as cm
conf_mat = cm(y_true, y_pred)
sns.heatmap(conf_mat, annot=True)# Get the actual responses from the dataset
actual_response <- churn$has_churned
# Get the "most likely" responses from the model
predicted_response <- round(fitted(mdl_churn_vs_relationship))
# Create a table of counts
outcomes <- table(predicted_response, actual_response)
# See the result
outcomes
matrix_confusion = confusion_matrix(y_test, y_pred)
sns.heatmap(matrix_confusion, square=True, annot=True, cmap='Blues', fmt='d', cbar=Fals