Understanding Ordinary Least Squares Regression: Definition, Explanations, Examples & Code
The Ordinary Least Squares Regression (OLSR) is a regression algorithm used in supervised learning. It is a type of linear least squares method utilized for estimating the unknown parameters in a linear regression model. As a regression algorithm, OLSR is used to predict continuous numerical values. It is widely used in various fields, including finance, economics, engineering, and social sciences, to ana
Understanding Partial Least Squares Regression: Definition, Explanations, Examples & Code
Partial Least Squares Regression (PLSR) is a dimensionality reduction technique used in supervised learning. PLSR is a method for constructing predictive models when the factors are many and highly collinear. It is a regression-based approach that seeks to find the directions in the predictor space that explain the maximum covariance between the predictors and the response.
Partial Least Squares Regressi
Understanding Perceptron: Definition, Explanations, Examples & Code
The Perceptron is a type of Artificial Neural Network that operates as a linear classifier. It makes its predictions based on a linear predictor function combining a set of weights with the feature vector. This algorithm falls under the category of Supervised Learning methods.
Perceptron: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Artificial Neural Network
The Perceptron is a type of Ar
Understanding Principal Component Regression: Definition, Explanations, Examples & Code
Principal Component Regression (PCR) is a dimensionality reduction technique that combines Principal Component Analysis (PCA) and regression. It first extracts the principal components of the predictors and then performs a linear regression on these components. PCR is a supervised learning method that can be used to improve the performance of regression models by reducing the number of predictors and removin
Understanding Projection Pursuit: Definition, Explanations, Examples & Code
Projection Pursuit is a type of dimensionality reduction algorithm that involves finding the most "interesting" possible projections in multidimensional data. It is a statistical technique that can be used for various purposes, such as data visualization, feature extraction, and exploratory data analysis. The algorithm uses a criterion function to identify the most informative projections, which can be either supervised
Understanding Quadratic Discriminant Analysis: Definition, Explanations, Examples & Code
Quadratic Discriminant Analysis (QDA) is a dimensionality reduction algorithm used for classification tasks in supervised learning. QDA generates a quadratic decision boundary by fitting class conditional densities to the data and using Bayes’ rule. As a result, QDA is a useful tool for solving classification problems with non-linear decision boundaries.
Quadratic Discriminant Analysis: Introduction
D
Understanding Radial Basis Function Network: Definition, Explanations, Examples & Code
The Radial Basis Function Network (RBFN) is a type of Artificial Neural Network that uses radial basis functions as activation functions. It is a supervised learning algorithm, which means that it requires input and output data to train the network. The RBFN is known for its ability to approximate any function to arbitrary levels of accuracy and is commonly used for function approximation, classification, and
Understanding Random Forest: Definition, Explanations, Examples & Code
Random Forest is an ensemble machine learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes of the individual trees. It falls under the category of supervised learning.
Random Forest: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Ensemble
The Random Forest algorithm is a popular and effective
Understanding Recurrent Neural Network: Definition, Explanations, Examples & Code
The Recurrent Neural Network, also known as RNN, is a type of Deep Learning algorithm. It is characterized by its ability to form directed graph connections between nodes along a sequence, which allows it to exhibit temporal dynamic behavior. RNN has become increasingly popular in recent years due to its ability to handle sequential data of varying lengths. RNN can be trained using both Supervised and Unsupervised
Understanding Ridge Regression: Definition, Explanations, Examples & Code
Ridge Regression is a regularization method used in Supervised Learning. It uses L2 regularization to prevent overfitting by adding a penalty term to the loss function. This penalty term limits the magnitude of the coefficients in the regression model, which can help prevent overfitting and improve generalization performance.
Ridge Regression: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervise
Understanding Rotation Forest: Definition, Explanations, Examples & Code
Rotation Forest is an ensemble learning method that generates individual decision trees based on differently transformed subsets of the original features. The transformations aim to enhance diversity among the individual models, increasing the robustness of the resulting ensemble model. It falls under the category of supervised learning.
Rotation Forest: Introduction
Domains
Learning Methods
Type
Machine Learning
Understanding Stacked Generalization: Definition, Explanations, Examples & Code
Stacked Generalization is an ensemble learning method used in supervised learning. It is designed to reduce the biases of estimators and is accomplished by combining them.
Stacked Generalization: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Ensemble
Stacked Generalization, also known as Stacking, is an ensemble learning method that involves combining multiple base estimators t
Understanding Stepwise Regression: Definition, Explanations, Examples & Code
Stepwise Regression is a regression algorithm that falls under the category of supervised learning. It is a method of fitting regression models in which the choice of predictive variables is carried out automatically.
Stepwise Regression: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Regression
Stepwise Regression is a regression algorithm used in supervised learning that automati
Understanding Support Vector Machines: Definition, Explanations, Examples & Code
Support Vector Machines (SVM), is an instance-based, supervised learning algorithm used for classification. The algorithm finds the hyperplane that maximizes the margin between classes in the training data. In other words, SVM is a classifier that separates the data points of different classes by drawing a decision boundary or hyperplane in a high-dimensional space. This decision boundary is chosen in such a way th
Understanding Support Vector Regression: Definition, Explanations, Examples & Code
Support Vector Regression (SVR) is an instance-based, supervised learning algorithm which is an extension of Support Vector Machines (SVM) for regression problems. SVR is a powerful technique used in machine learning for predicting continuous numerical values. Unlike traditional regression algorithms, SVR uses support vectors to map data points into a high-dimensional feature space in order to capture non-linear
Understanding Weighted Average: Definition, Explanations, Examples & Code
The Weighted Average algorithm is an ensemble method of calculation that assigns different levels of importance to different data points. It can be used in both supervised learning and unsupervised learning scenarios.
Weighted Average: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised, Unsupervised
Ensemble
The Weighted Average algorithm is a powerful calculation method that assigns diff