Overfitting due to high model complexity
Overfitting occurs when a machine learning model learns patterns from the training data too well, including noise and random fluctuations. This leads to poor generalization on new, unseen data. High….
Tag: Regularization
Long Short-Term Memory Networks (LSTMs)
Long Short-Term Memory Networks (LSTMs): Detailed Explanation Long Short-Term Memory Networks (LSTMs) are a specialized type of Recurrent Neural Networks (RNNs) that are specifically designed to overcome the limitations of….
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs): Detailed Explanation Convolutional Neural Networks (CNNs) are a class of deep neural networks specifically designed to work with grid-like data, such as images, audio, and video…..
Introduction to Neural Networks
Introduction to Neural Networks: Detailed Explanation A Neural Network (NN) is a computational model inspired by the way biological neural networks in the human brain process information. It is a….
Gradient Boosting (XGBoost, LightGBM, CatBoost)
Gradient Boosting (XGBoost, LightGBM, CatBoost) in Machine Learning 1. Introduction to Gradient Boosting Gradient Boosting is a powerful ensemble learning technique used in machine learning for classification and regression tasks…..
Support Vector Machines (SVM)
Support Vector Machines (SVM) in Machine Learning 1. Introduction to Support Vector Machines (SVM) Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression problems. SVM….
Logistic Regression
Logistic Regression in Machine Learning 1. Introduction to Logistic Regression Logistic Regression is a Supervised Learning algorithm used for classification problems. Unlike Linear Regression, which predicts continuous values, Logistic Regression….
Feature Scaling in Machine Learning
Feature Scaling in Machine Learning Introduction Feature scaling is a crucial step in the data preprocessing stage of machine learning. It ensures that all numerical features in the dataset have….
Underfitting vs Overfitting
Underfitting vs Overfitting in Machine Learning Introduction One of the biggest challenges in machine learning is building a model that can generalize well to unseen data. The two common problems….
Bias-Variance Tradeoff
Bias-Variance Tradeoff in Machine Learning Introduction The bias-variance tradeoff is a fundamental concept in machine learning that describes the tradeoff between two sources of error that affect model performance: Understanding….