“matrice de confusion” Réponses codées

Matrice de confusion Python

By definition, entry i,j in a confusion matrix is the number of 
observations actually in group i, but predicted to be in group j. 
Scikit-Learn provides a confusion_matrix function:

from sklearn.metrics import confusion_matrix
y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
confusion_matrix(y_actu, y_pred)
# Output
# array([[3, 0, 0],
#        [0, 1, 2],
#        [2, 1, 3]], dtype=int64)
Bored Coder

tracer la matrice de confusion

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
JJSSEECC

matrice de confusion

# 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))
josh.ipynb

Matrice de confusion

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)
shadow ekbote

R Matrice de confusion

# 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
Successful Salmon

matrice de confusion


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

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