mport matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
classifiers = {
"Naive Bayes": GaussianNB(),
"LogisiticRegression": LogisticRegression(),
"KNearest": KNeighborsClassifier(),
"Support Vector Classifier": SVC(),
"DecisionTreeClassifier": DecisionTreeClassifier(),
}
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
f, axes = plt.subplots(1, 5, figsize=(20, 5), sharey='row')
for i, (key, classifier) in enumerate(classifiers.items()):
y_pred = classifier.fit(X_train, y_train).predict(X_test)
cf_matrix = confusion_matrix(y_test, y_pred)
disp = ConfusionMatrixDisplay(cf_matrix,
display_labels=iris.target_names)
disp.plot(ax=axes[i], xticks_rotation=45)
disp.ax_.set_title(key)
disp.im_.colorbar.remove()
disp.ax_.set_xlabel('')
if i!=0:
disp.ax_.set_ylabel('')
f.text(0.4, 0.1, 'Predicted label', ha='left')
plt.subplots_adjust(wspace=0.40, hspace=0.1)
f.colorbar(disp.im_, ax=axes)
plt.show()
