Compute Classification Report and Confusion Matrix in Python

Classification Report and Confusion Matrix are used to check machine learning model's performance during model development. These help us understand the accuracy of predictions and tells areas of improvement. In this article, we will learn how to compute these metrics in Python using a simple example.

Understanding the Classification Report and Confusion Matrix

The Classification Report summarizes the performance of a classification model. It includes key metrics such as:

1. Precision : Measures the accuracy of positive predictions.
2. Recall : Indicates how many actual positives were correctly identified.
3. F1-Score : Balances precision and recall into a single score.
4. Support : Shows the number of samples for each class.

These metrics help us understand how well the model performs for each class.

Confusion Matrix is a table that compares the model's predictions against the actual values. It highlights where the model succeeds and where it makes mistakes. For a binary classification problem like "Yes" or "No" the confusion matrix looks like this:

• True Positive (TP) : Correctly predicted as "Yes."
• False Negative (FN) : Incorrectly predicted as "No" when it was actually "Yes."
• False Positive (FP) : Incorrectly predicted as "Yes" when it was actually "No."
• True Negative (TN) : Correctly predicted as "No".

This matrix helps identify patterns in errors and evaluate overall accuracy.

Step-by-Step Guide to Compute Metrics in Python

We will use Python and the scikit-learn library to compute the Classification Report and Confusion Matrix.

Step 1: Import Necessary Libraries

We need to import the required library scikit learn.

from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris

Step 2: Load Dataset

We’ll use the Iris dataset which contains data about flowers and their species.

data = load_iris()
X = data.data  
y = data.target 

Step 3: Split Data into Training and Testing Sets

To evaluate the model on unseen data we split the dataset into 70% training data and 30% testing data.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

Step 4: Train a Model

We’ll use logistic regression for classification.

model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

Output:

Step 5: Make Predictions

After training the model we use it to predict labels for the test data.

y_pred = model.predict(X_test)

Step 6: Compute the Confusion Matrix

Now let’s calculate the confusion matrix to analyze the results.

conf_matrix = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:")
print(conf_matrix)

Output:

This means:

• For class 0 (Setosa) all predictions were correct.
• For class 1 (Versicolor) there was 1 false negative.
• For class 2 (Virginica) there was 1 false positive.

Step 7: Generating the Classification Report

Finally let’s generate the classification report to get detailed performance metrics.

class_report = classification_report(y_test, y_pred, target_names=data.target_names)
print("Classification Report:")
print(class_report)

Output:

This above classification report shows that the model performs good with high precision, recall and F1-scores across all classes. You can easily compute Classification Report and Confusion Matrix using the scikit-learn Python library.