Comparing Popular Classification Algorithms
In this tutorial, we will compare and contrast six popular classification algorithms, including logistic regression, decision trees, random forests, support vector machines (SVM), naive Bayes, and K-nearest neighbors (KNN). We will discuss the data assumptions, model complexity, interpretability, and ease of implementation for each algorithm.
🦊 I also invite you to explore my tutorials on supervised and unsupervised learning, which can be found under the following topic lists: ‘Topics on Supervised Learning’, ‘Topics on Unsupervised Learning’, and ‘General Topics on Machine Learning’.
📌 Logistic Regression:
Logistic regression is a classification algorithm used to assign observations to a discrete set of classes. The algorithm builds a model that estimates the probability of each class using input features. The model is then used to predict the class of new observations. Logistic regression is a linear model, so the algorithm is based on a linear equation. The equation estimates the probability of the class label using a set of input features. The coefficients of the equation are estimated using maximum likelihood estimation. The algorithm can be used for binary classification, where the classes are labelled 0 and 1, or for multi-class classification, where the classes are labelled 0, 1, 2, 3, etc.
Decision tree algorithms are powerful tools for both classification and regression tasks. The main idea behind them…
