PYTHON

plotting confusion matrix

from sklearn.metrics import confusion_matrix
matrix_confusion = confusion_matrix(y_test, y_pred)
sns.heatmap(matrix_confusion, square=True, annot=True, cmap='Blues', fmt='d', cbar=False

Confusion matrix

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)

confusion matrix code

cm = confusion_matrix(y_test, clf_pred)
print(cm)
print(accuracy_score(y_test, clf_pred))
plt.figure(figsize=(10,5))
sns.heatmap(cm, annot=True, fmt="d", linewidths=.5)
plt.show()

confusion matrix

# 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))

r confusion matrix

# 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

confusion matrix

matrix_confusion = confusion_matrix(y_test, y_pred)
sns.heatmap(matrix_confusion, square=True, annot=True, cmap='Blues', fmt='d', cbar=Fals