When exploring the foundations of machine learning, one of the most frequently encountered algorithms is logistic regression. It is widely used for binary classification tasks and serves as a stepping stone to more complex models. Yet a common question arises for newcomers: Is logistic regression supervised learning? The short and definitive answer is yes—logistic regression … Read more
Logistic regression is one of the most popular and widely used algorithms for binary classification problems in machine learning. But beyond making predictions, understanding which features matter most can help improve model interpretability, trust, and even feature engineering. This brings us to the concept of feature importance in logistic regression. In this blog post, we … Read more
Logistic regression is one of the most widely used machine learning algorithms for classification problems. Unlike linear regression, which predicts continuous values, logistic regression predicts categorical outcomes (e.g., yes/no, spam/not spam, diseased/healthy). But when should you use logistic regression? Understanding its applications, strengths, and limitations is crucial for building effective predictive models. In this guide, … Read more
Logistic regression is one of the most widely used statistical and machine learning algorithms for classification problems. It is simple, interpretable, and effective in many real-world applications. However, one limitation of logistic regression is that it assumes a linear relationship between the independent variables (features) and the log-odds of the dependent variable (target). This raises … Read more
