نحوه استفاده از ماتریس سردرگمی در Scikit-Learn (با مثال پایتون)

import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split

def to_target(x):
    """Map targets to target names"""    
    return list(dataset.target_names)[x]

# Load data
dataset = load_breast_cancer()
df = pd.DataFrame(dataset.data,columns=dataset.feature_names)
df['target'] = pd.Series(dataset.target)
df['target_names'] = df['target'].apply(to_target)


# Define predictor and predicted datasets
X = df.drop(['target','target_names'], axis=1).values
y = df['target_names'].values

# split taining and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)

# train the model
knn = KNeighborsClassifier(n_neighbors=8)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)

# compute accuracy of the model
knn.score(X_test, y_test)

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_test,y_pred)
cm

array([[ 57,   7],
       [  5, 102]])

import matplotlib.pyplot as plt
from sklearn.metrics import plot_confusion_matrix

color = 'white'
matrix = plot_confusion_matrix(knn, X_test, y_test, cmap=plt.cm.Blues)
matrix.ax_.set_title('Confusion Matrix', color=color)
plt.xlabel('Predicted Label', color=color)
plt.ylabel('True Label', color=color)
plt.gcf().axes[0].tick_params(colors=color)
plt.gcf().axes[1].tick_params(colors=color)
plt.show()

ImportError: cannot import name 'plot_confusion_matrix' from 'sklearn.metrics'

FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.
  warnings.warn(msg, category=FutureWarning)

import matplotlib.pyplot as plt
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_test, y_pred, labels=knn.classes_)
color = 'white'
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=knn.classes_)
disp.plot()
plt.show()

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred))

#Import the necessary libraries
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score

# Load the wine dataset
X, y = load_wine(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.25)
 
# Train the model
clf = RandomForestClassifier(random_state=23)
clf.fit(X_train, y_train)
 
# Predict using the test data
y_pred = clf.predict(X_test)
 
# Compute the confusion matrix
cm = confusion_matrix(y_test,y_pred)
 
# Plot the confusion matrix.
sns.heatmap(cm,
            annot=True)
plt.ylabel('Prediction',fontsize=13)
plt.xlabel('Actual',fontsize=13)
plt.title('Confusion Matrix',fontsize=17)
plt.show()
 
# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy   :", accuracy)