In the context of artificial neural networks (ANNs), MNIST refers to the MNIST dataset, which is often used as a benchmark for training and testing ANN models, particularly for image classification tasks. The MNIST dataset consists of a large collection of grayscale images of handwritten digits from 0 to 9. Each image is a 28×28…
A Multi-Layer Perceptron (MLP) is a type of artificial neural network that consists of multiple layers of nodes (perceptrons). Unlike a single-layer perceptron, an MLP has one or more hidden layers between the input and output layers. Each node in a layer is connected to every node in the subsequent layer. Here’s a basic overview…
A perceptron is one of the simplest forms of artificial neural networks. It’s a binary classifier that takes multiple binary inputs and produces a single binary output. Here’s how it works:
RandomizedSearchCV is a method provided by scikit-learn for hyperparameter tuning and model selection through cross-validation. It’s similar to GridSearchCV, but instead of exhaustively searching through all possible combinations of hyperparameters, it randomly samples a fixed number of hyperparameter settings from specified distributions. Here’s a basic overview of how RandomizedSearchCV works: Here’s a basic example of…
Tomek Link Undersampling is a technique used to address class imbalance in machine learning datasets. It involves identifying Tomek links, which are pairs of instances from different classes that are nearest neighbors of each other, and removing instances from the majority class that form these links. The main idea behind Tomek Link Undersampling is to…
SMOTE (Synthetic Minority Over-sampling Technique) is an upsampling technique used in machine learning to address the class imbalance problem, which occurs when the number of instances of one class (minority class) is significantly lower than the number of instances of the other class (majority class) in a dataset. This class imbalance can lead to biased…
Bagging (Bootstrap Aggregating) and Boosting are both ensemble learning techniques that aim to improve the predictive performance of machine learning models by combining multiple base learners. However, they differ in their approach to training and how they leverage the base learners’ predictions to improve model performance. Bagging focuses on reducing variance, whereas Boosting focuses on…
XGBoost stands for eXtreme Gradient Boosting, and it’s an optimized and highly scalable implementation of the Gradient Boosting framework. Developed by Tianqi Chen and now maintained by the Distributed (Deep) Machine Learning Community, XGBoost has gained widespread popularity in machine learning competitions and real-world applications due to its efficiency, flexibility, and outstanding performance. XGBoost Parameters…
Gradient Boosting is another ensemble learning technique used for classification and regression tasks and has its own specific way of building the ensemble of weak learners. Here’s a brief overview of Gradient Boosting: Gradient Boosting typically produces more accurate models compared to AdaBoost but can be more computationally expensive and prone to overfitting, especially with…
AdaBoost (Adaptive Boosting) is a popular ensemble learning algorithm used for classification and regression tasks. It works by combining multiple weak learners (typically decision trees, often referred to as “stumps”) to create a strong learner. Here’s how it generally works: AdaBoost is effective because it focuses on improving the classification of difficult examples by giving…
