XGBoost (eXtreme Gradient Boosting) is a scalable and efficient implementation of gradient boosting for supervised learning. It has gained popularity for its performance and speed, often winning machine learning competitions. Here are the key concepts and features of XGBoost: Key Concepts: Key Parameters: Example in Python: Here’s a basic example of using XGBoost in Python…
this is about combining multiple models in random forest we use multiple decision trees in boostng we use this methods Ensemble learning is a technique in machine learning where multiple models (often called “weak learners”) are trained to solve the same problem and combined to get better performance. The primary goal of ensemble methods is…
A Markov Decision Process (MDP) is a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision-maker. It provides a formalism for modeling the environment in reinforcement learning. An MDP is defined by: The goal in an MDP is to find a policy that maximizes…
SVMs shine with small to medium-sized nonlinear datasets (i.e., hundreds to thousands of instances), especially for classification tasks. this is hyper plane Support Vector Machines (SVM) are a powerful and versatile supervised machine learning algorithm used for classification and regression tasks. They are particularly well-suited for binary classification problems. SVMs work by finding the hyperplane…
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پس این معادله لاپلاس میباشد The Laplace equation is a second-order partial differential equation named after the French mathematician Pierre-Simon Laplace. It is commonly used in physics and engineering to describe the behavior of electric, gravitational, and fluid potentials. The equation is given by: $$\nabla^2 \phi = 0$$ or in Cartesian coordinates: $$\frac{\partial^2 \phi}{\partial x^2}…