A Comparative Analysis: Logistic Regression vs. Artificial Neural Networks

In the realm of machine learning and data science, the selection of an appropriate algorithm greatly influences the accuracy and efficiency of a predictive model. Logistic Regression and Artificial Neural Networks (ANN) are two widely used techniques in classification tasks. This article aims to provide a comprehensive comparison between these two approaches in a tabular format, highlighting their strengths and limitations. Additionally, real-world scenarios where Logistic Regression outperforms ANN will be discussed.

Comparative Analysis

When Logistic Regression Performs Better

While Artificial Neural Networks offer remarkable capabilities for intricate problems, there are instances where Logistic Regression excels:

• Small Datasets:

Logistic Regression is more effective when dealing with limited data. ANN requires substantial data for meaningful training, making Logistic Regression a favorable choice when the dataset…