COMPARATIVE ANALYSIS OF LOGISTIC REGRESSION, RANDOM FOREST. NAÏVE BAYES ALGORITHMS IN DATA MINING

Abstract

Data mining techniques have become essential tools for discovering patterns, trends, and insights from vast datasets across various domains. Among the numerous algorithms used in this field, Logistic Regression, Random Forest, and Naïve Bayes are prominent for classification tasks. Each algorithm has its own strengths and weaknesses, depending on the characteristics of the data and the objectives of the analysis. Logistic Regression, a statistical method for binary classification, is renowned for its simplicity and interpretability. It models the relationship between input features and a binary outcome using a sigmoid function, making it highly effective when the relationship between the predictors and the target variable is linear. However, it struggles with non-linear data and is sensitive to multicollinearity among input variables. Random Forest, an ensemble learning method based on decision trees, provides robustness and flexibility. By combining multiple decision trees into a "forest," it enhances predictive performance and reduces overfitting through bagging and random feature selection. Random Forest excels with complex, non-linear datasets and can handle missing data and feature interactions effectively, but it can be computationally expensive and less interpretable compared to simpler models.Naïve Bayes, a probabilistic classifier based on Bayes' Theorem, assumes strong independence between features, making it computationally efficient. It is particularly useful in text classification and spam detection, where feature independence is more realistic. However, Naïve Bayes can be less accurate when the independence assumption is violated, especially with highly correlated features.