Implementation: score() method vs accuracy_score() function in python

Below is an example of implementation demonstrating the difference between two evaluation methods using a simple logistic regression model in scikit-learn –

Step 1: Importing the necessary libraries

We will import necessary modules for logistic regression, accuracy evaluation, iris dataset loading, and train-test splitting, facilitating the development and assessment of a logistic regression model for classification tasks.

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

Step 2 : Loading the iris dataset

Once ,we have loaded the necessary libraries, we will be loading Iris Dataset into variables “X” and “y” representing the feature matrix and target labels, respectively.

# Load dataset
iris = load_iris()
X, y = iris.data, iris.target

Step 3 : Splitting the dataset into training and testing sets

This code splits the dataset into training and testing sets, with 80% of the data reserved for training (X_train, y_train) and 20% for testing (X_test, y_test), ensuring consistency in random splitting with a specified seed value (random_state=42).

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 4 : Create and Train a logistic Regression model and train on training dataset

We create a logistic regression model instance and trains it on the training data (X_train, y_train) to learn the relationships between the features and target labels.

# Create and train a model
model = LogisticRegression()
model.fit(X_train, y_train)

Step 5 : Model Evaluation

score() method

Then, we evaluate the trained logistic regression model’s performance on the test data (X_test, y_test) using the score() method, which computes the accuracy of the model’s predictions compared to the actual target labels. It then prints out the accuracy score.

# Method 1: Using score method
score = model.score(X_test, y_test)
print(f"Score method - Accuracy: {score}")

Output :

Score method - Accuracy: 1.0

accuracy_score()

The provided code generates predictions (y_pred) from the trained logistic regression model on the test data (X_test). Then, it computes the accuracy of the model’s predictions compared to the actual target labels (y_test) using the accuracy_score function, which measures the proportion of correctly predicted labels. Finally, it prints out the accuracy score obtained from the accuracy_score function.

# Get predictions from the model
y_pred = model.predict(X_test)
# Method 2: Using accuracy_score function
accuracy = accuracy_score(y_test, y_pred)
print(f"accuracy_score function - Accuracy: {accuracy}")

Output :

accuracy_score function - Accuracy: 1.0

In this implementation, we train a logistic regression model on the Iris dataset and then evaluate its accuracy using both the score() method and accuracy_score() function. The output will demonstrate the same accuracy value obtained using both approaches, showcasing their equivalence in evaluating model accuracy.