2024-01-23 17:11:14
Here are 10 Machine Learning algorithms that are very popular and help solve countless business problems.
Linear Regression: Algorithm used to model the relationship between a dependent variable and one or more independent variables. They can be used in statistical modeling (studying the relationship between variables) or Machine Learning (building predictive models). There are several types of regression techniques: linear, logistic, polynomial regression, etc…
Logistic Regression: Despite the name, it is a classification algorithm used to estimate discrete values (binary values such as 0/1, yes/no, true/false) based on a given set of independent variables.
Decision Trees: A tree-like model used for classification and regression. It splits data based on certain conditions, making it intuitive and easy to understand.
Random Forest: A powerful supervised learning model used for both classification and regression, but is most commonly used in classification problems. It is a set of decision trees.
AdaBoost (Adaptive Boosting): A boosting algorithm that combines several weak estimators (typically decision trees) to create a strong classifier.
Gradient Boosting Machines (GBM): An ensemble technique that builds models sequentially, each new model correcting errors made by the previous ones. Variants include XGBoost, LightGBM and CatBoost.
Naive Bayes: A family of probabilistic classifiers based on applying Bayes’ theorem with strong (naive) assumptions of independence between features.
Support Vector Machine (SVM): A powerful supervised learning model used for both classification and regression, but is most commonly used in classification problems. It works by finding the hyperplane that best divides a set of data into classes.
K-Nearest Neighbors (KNN): A simple and easy-to-implement supervised machine learning algorithm that can be used for both classification and regression. It is a non-parametric learning algorithm.
Clustering K-Means: An unsupervised learning algorithm used to cluster (group) data by similarity. It partitions data into K distinct clusters based on the distance to a cluster’s centroid.
Many other more advanced Machine Learning algorithms are variations, compositions, or portmanteaus of these algorithms.
David Matos
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