Understanding Gradient Boosted Regression Trees: Definition, Explanations, Examples & Code
The Gradient Boosted Regression Trees (GBRT), also known as Gradient Boosting Machine (GBM), is an ensemble machine learning technique used for regression problems.
This algorithm combines the predictions of multiple decision trees, where each subsequent tree improves the errors of the previous tree. The GBRT algorithm is a supervised learning method, where a model learns to predict an outcome variable f
Understanding Gradient Boosting Machines: Definition, Explanations, Examples & Code
The Gradient Boosting Machines (GBM) is a powerful ensemble machine learning technique used for regression and classification problems. It produces a prediction model in the form of an ensemble of weak prediction models. GBM is a supervised learning method that has become a popular choice for predictive modeling thanks to its performance and flexibility.
Gradient Boosting Machines: Introduction
Domains
Lea
Understanding Hopfield Network: Definition, Explanations, Examples & Code
The Hopfield Network is a type of artificial neural network that serves as content-addressable memory systems with binary threshold nodes. As a recurrent neural network, it has the ability to store and retrieve patterns in a non-destructive manner. The learning methods used in Hopfield Network include both supervised and unsupervised learning.
Hopfield Network: Introduction
Domains
Learning Methods
Type
Machine
Understanding Iterative Dichotomiser 3: Definition, Explanations, Examples & Code
The Iterative Dichotomiser 3 (ID3) is a decision tree algorithm invented by Ross Quinlan used to generate a decision tree from a dataset. It is a type of supervised learning method, where the algorithm learns from a labeled dataset and creates a tree-like model of decisions and their possible consequences. The ID3 algorithm is widely used in machine learning and data mining for classification problems.
Iterative
Understanding k-Nearest Neighbor: Definition, Explanations, Examples & Code
The k-Nearest Neighbor (kNN) algorithm is a simple instance-based algorithm used for both supervised and unsupervised learning. It stores all the available cases and classifies new cases based on a similarity measure. The algorithm is named k-Nearest Neighbor because classification is based on the k-nearest neighbors in the training set. kNN is a type of lazy learning algorithm, meaning that it doesn't have a model to t
Understanding Learning Vector Quantization: Definition, Explanations, Examples & Code
The Learning Vector Quantization (LVQ) algorithm is a prototype-based supervised classification algorithm. It falls under the category of instance-based machine learning algorithms and operates by classifying input data based on their similarity to previously seen data. LVQ relies on supervised learning, where a training dataset with known class labels is used to train the algorithm.
Learning Vector Quantiza
Understanding Least Absolute Shrinkage and Selection Operator: Definition, Explanations, Examples & Code
The Least Absolute Shrinkage and Selection Operator (LASSO), is a regularization method used in supervised learning. It performs both variable selection and regularization, making it a valuable tool for regression analysis. With LASSO, the algorithm shrinks the less important feature coefficients to zero, effectively selecting only the most relevant features in the model.
Least Absolute Sh
Understanding Least-Angle Regression: Definition, Explanations, Examples & Code
Least-Angle Regression (LARS) is a regularization algorithm used for high-dimensional data in supervised learning. It is efficient and provides a complete piecewise linear solution path.
Least-Angle Regression: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Regularization
Least-Angle Regression (LARS) is a powerful regression algorithm for high-dimensional data that is both effi
Understanding LightGBM: Definition, Explanations, Examples & Code
LightGBM is an algorithm under Microsoft's Distributed Machine Learning Toolkit. It is a gradient boosting framework that uses tree-based learning algorithms. It is an ensemble type algorithm that performs supervised learning. LightGBM is designed to be distributed and efficient, offering faster training speed and higher efficiency, lower memory usage, better accuracy, the ability to handle large-scale data, and supports parallel
Understanding Locally Estimated Scatterplot Smoothing: Definition, Explanations, Examples & Code
Locally Estimated Scatterplot Smoothing (LOESS) is a regression algorithm that uses local fitting to fit a regression surface to data. It is a supervised learning method that is commonly used in statistics and machine learning. LOESS works by fitting a polynomial function to a small subset of the data, known as a neighborhood, and then using this function to predict the output for a new input. This
Understanding Locally Weighted Learning: Definition, Explanations, Examples & Code
Locally Weighted Learning (LWL) is an instance-based supervised learning algorithm that uses nearest neighbors for predictions. It applies a weighting function that gives more influence to nearby points, making it useful for non-linear regression problems.
Locally Weighted Learning: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Instance-based
Locally Weighted Learning, or LW
Understanding Long Short-Term Memory Network: Definition, Explanations, Examples & Code
The Long Short-Term Memory Network (LSTM) is a type of deep learning algorithm capable of learning order dependence in sequence prediction problems. As a type of recurrent neural network, LSTM is particularly useful in tasks that require the model to remember and selectively forget information over an extended period. LSTM is trained using supervised learning methods and is useful in a wide range of natural
Understanding M5: Definition, Explanations, Examples & Code
M5 is a tree-based machine learning method that falls under the category of decision trees. It is primarily used for supervised learning and produces either a decision tree or a tree of regression models in the form of simple linear functions.
M5: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Decision Tree
M5 is a powerful decision tree-based machine learning algorithm that is commonly used in the
Understanding Mixture Discriminant Analysis: Definition, Explanations, Examples & Code
Mixture Discriminant Analysis (MDA) is a dimensionality reduction method that extends linear and quadratic discriminant analysis by allowing for more complex class conditional densities. It falls under the category of supervised learning algorithms.
Mixture Discriminant Analysis: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Dimensionality Reduction
Mixture Discriminant
Understanding Multilayer Perceptrons: Definition, Explanations, Examples & Code
The Multilayer Perceptrons (MLP) is a type of Artificial Neural Network (ANN) consisting of at least three layers of nodes, namely an input layer, a hidden layer, and an output layer. MLP is a powerful algorithm used in supervised learning tasks, such as classification and regression. Its ability to efficiently learn complex non-linear relationships and patterns in data makes it a popular choice in the field of mach
Understanding Multinomial Naive Bayes: Definition, Explanations, Examples & Code
Name: Multinomial Naive Bayes
Definition: A variant of Naive Bayes classifier that is suitable for discrete features.
Type: Bayesian
Learning Methods:
* Supervised Learning
Multinomial Naive Bayes: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Bayesian
Name: Multinomial Naive Bayes
Definition: A variant of Naive Bayes classifier that is suitable for discrete features.
T
Understanding Multivariate Adaptive Regression Splines: Definition, Explanations, Examples & Code
Multivariate Adaptive Regression Splines (MARS) is a regression analysis algorithm that models complex data by piecing together simpler functions. It falls under the category of supervised learning methods and is commonly used for predictive modeling and data analysis.
Multivariate Adaptive Regression Splines: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Regressi
Understanding Naive Bayes: Definition, Explanations, Examples & Code
Naive Bayes is a Bayesian algorithm used in supervised learning to classify data. It is a simple probabilistic classifier that applies Bayes' theorem with strong independence assumptions between the features.
Naive Bayes: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Bayesian
Naive Bayes is a popular algorithm used in machine learning for classification tasks. It is a simple probabilistic