Top 10 Commonly Used Machine Learning Algorithms

Decision Trees and Random Forests

Decision trees and random forests are two of the most commonly used machine learning algorithms. Decision trees are used to build models that can be used to make predictions about future events. Random forests are used to create models that can be used to predict the outcomes of future events.

Neural Networks

Neural networks are a type of machine learning algorithm that is used to model complex patterns in data. Neural networks are similar to other machine learning algorithms. Still, they are composed of a large number of interconnected processing nodes, or neurons, that can learn to recognise patterns of input data.

Boosting

Boosting is a machine learning technique that combines multiple weak learners to create a strong learner. Boosting is an iterative process where each weak learner is trained on a subset of the data. The aim is to correct the errors of the previous learner and build on top of it, creating a stronger and stronger learner.

K-Nearest Neighbors

The k-nearest neighbours (KNN) algorithm is one of the simplest and most commonly used machine learning algorithms. It is a non-parametric, lazy learning algorithm that can be used for both classification and regression. The KNN algorithm works by storing all training data points and then classifying new data points based on the similarity of their features.

Naive Bayes

Naive Bayes is a simple yet powerful machine-learning algorithm that can be used for both classification and regression tasks. It is a probabilistic algorithm that makes use of Bayesian inference to make predictions. The main advantage of using Naive Bayes is that it is relatively easy to implement and understand. Additionally, it has been shown to be quite effective in practice, especially with high-dimensional data sets.

Dimensionality Reduction

Dimensionality reduction is a type of unsupervised learning algorithm that can be used to reduce the number of features in a dataset. This can be useful for visualising data, increasing computational efficiency, or reducing the amount of data required for training. Common dimensionality reduction algorithms include Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

Model Ensembles

Ensemble learning is a powerful machine learning algorithm that combines the predictions of multiple models to produce more accurate results. Ensemble learning is often used in competition situations where the goal is to outperform other models or in cases where the individual models making up the ensemble are less accurate than we would like them to be.