In recent years, I have had conversations with colleagues where we have noticed that classical ML models are often neglected in the world of GenAI and deep learning.
But these classical models are our foundation.
Here are some classic models and their brief descriptions.
Supervised Learning Algorithms
Linear regression
- explanation: Used to predict a continuous target variable based on one or more input features.
Logistic regression
- explanation: Used for binary classification tasks, predicting the probability of a categorical dependent variable.
Decision Tree
- explanation: A tree-like model used for classification and regression tasks, which splits data into subsets based on feature values.
Support Vector Machine (SVM)
- explanation: It is used for classification and regression tasks to find a hyperplane that best separates classes in a feature space.
k-Nearest Neighbor (k-NN)
- explanation: A simple instance-based learning algorithm used for classification and regression. It predicts based on the majority class or average of nearest neighbors.
Naive Bayes
- explanation: A probabilistic classifier based on Bayes' theorem, assuming independence between features.
Random Forest
- explanation: An ensemble learning method that builds multiple decision trees and integrates them to obtain more accurate and stable predictions.
Gradient Boosting Machine (GBM)
- explanation: An ensemble method that builds models in sequence, with each model correcting the errors of the previous model.
Artificial Neural Networks (ANN)
- explanationA set of algorithms loosely modeled on the human brain, designed to recognize patterns.
Unsupervised Learning Algorithms
k-Means Clustering
- explanation: A method for splitting data into k distinct clusters based on feature similarities.
Principal Component Analysis (PCA)
- explanation: A dimensionality reduction technique that transforms data into a set of orthogonal (uncorrelated) variables called principal components.
Feel free to leave a comment and let's discuss.
