Today, I worked up further on Logistic Regression.
Binary logistic regression, ordinal logistic regression, and multinomial logistic regression are the three primary subtypes of logistic regression.
When the dependent variable is binary, Binary Logistic Regression—the most popular of the three forms of logistic regression—is employed. It can only make two assumptions.
Accuracy Score
The accuracy score is a commonly used metric to measure the performance of a classification model. It calculates the percentage of correct predictions made by the model. The accuracy score ranges from 0 to 1, where 1 represents a perfect prediction.
Classification Report
The classification report provides a comprehensive summary of the model’s performance for each class in a classification problem. It includes metrics such as precision, recall, and F1-score, which help evaluate the model’s ability to correctly classify instances of each class.
The code provided consists of two main parts: importing the necessary modules and evaluating the logistic regression model’s performance.
First, we import the accuracy_score and classification_report functions from the sklearn.metrics module using the from keyword.
Next, we have the report variable, which stores the classification report generated by the classification_report function. This function takes two arguments: y_test and y_pred. y_test represents the true labels of the test data, while y_pred represents the predicted labels generated by the logistic regression model.
Finally, I printed the accuracy score and the classification report using the print function.
Output –
Logistic Regression Model Accuracy: 0.9567341242149338
Classification Report:
precision recall f1-score support
0 0.00 0.00 0.00 62
1 0.96 1.00 0.98 1371
accuracy 0.96 1433
macro avg 0.48 0.50 0.49 1433
weighted avg 0.92 0.96 0.94 1433