Logistic Regression, Explained: A Visual Guide with Code Examples for Beginners
CLASSIFICATION ALGORITHM
⛳️ More [Classification ALGORITHM](https://medium.com/@samybaladram/list/classification-algorithms-b3586f0a772c), explained: · [Dummy Classifier](https://towardsdatascience.com/dummy-classifier-explained-a-visual-guide-with-code-examples-for-beginners-009ff95fc86e) · [K Nearest Neighbor Classifier](https://towardsdatascience.com/k-nearest-neighbor-classifier-explained-a-visual-guide-with-code-examples-for-beginners-a3d85cad00e1) · [Bernoulli Naive Bayes](https://towardsdatascience.com/bernoulli-naive-bayes-explained-a-visual-guide-with-code-examples-for-beginners-aec39771ddd6) · [Gaussian Naive Bayes](https://towardsdatascience.com/gaussian-naive-bayes-explained-a-visual-guide-with-code-examples-for-beginners-04949cef383c) · [Decision Tree Classifier](https://towardsdatascience.com/decision-tree-classifier-explained-a-visual-guide-with-code-examples-for-beginners-7c863f06a71e) ▶ [Logistic Regression](https://towardsdatascience.com/logistic-regression-explained-a-visual-guide-with-code-examples-for-beginners-81baf5871505) · [Support Vector Classifier](https://towardsdatascience.com/support-vector-classifier-explained-a-visual-guide-with-mini-2d-dataset-62e831e7b9e9) · [Multilayer Perceptron](https://towardsdatascience.com/multilayer-perceptron-explained-a-visual-guide-with-mini-2d-dataset-0ae8100c5d1c)
While some probabilistic-based machine learning models (like Naive Bayes) make bold assumptions about feature independence, logistic regression takes a more measured approach. Think of it as drawing a line (or plane) that separates two outcomes, allowing us to predict probabilities with a bit more flexibility.
Definition
Logistic Regression is a statistical method used for predicting binary outcomes. Despite its name, it's used for classification rather than regression. It estimates the probability that an instance belongs to a particular class. If the estimated probability is greater than 50%, the model predicts that the instance belongs to that class (or vice versa).
