To demonstrate the classification metrics, a random forest model will be trained. Importing the libraries:

In [2]:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import (accuracy_score, precision_score, recall_score,
                             f1_score, confusion_matrix, ConfusionMatrixDisplay, classification_report)

Making an imbalanced classification dataset.

In [4]:

X, y = make_classification(n_samples=1000, n_classes=3, n_informative=3,
                           weights=[0.1, 0.3, 0.6], random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

Fitting the model.

In [6]:

model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

y_pred = model.predict(X_test)

Displaying the confusion matrix.

In [8]:

cm = confusion_matrix(y_test, y_pred)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=["Class 0", "Class 1", "Class 2"])
disp.plot(cmap="Blues")
plt.show()

Printing the metrics.

In [10]:

report = classification_report(y_test, y_pred)

print(report)

              precision    recall  f1-score   support

           0       0.82      0.56      0.67        32
           1       0.82      0.82      0.82        87
           2       0.88      0.93      0.90       181

    accuracy                           0.86       300
   macro avg       0.84      0.77      0.80       300
weighted avg       0.85      0.86      0.85       300