Confusion Matrix
Confusion Matrix in Machine Learning 1. Introduction to Confusion Matrix A Confusion Matrix is a performance evaluation metric used in classification problems. It helps to understand how well a machine….
Tag: Logistic Regression
ROC Curve and AUC
ROC Curve and AUC in Machine Learning The ROC (Receiver Operating Characteristic) Curve and AUC (Area Under the Curve) are essential metrics for evaluating the performance of classification models, especially….
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….
Train-Test Split and Cross-Validation
Train-Test Split and Cross-Validation in Machine Learning In machine learning, evaluating the performance of a model is crucial to ensure it generalizes well to unseen data. Two widely used techniques….
Feature Selection Techniques
Feature Selection Techniques: A Comprehensive Guide Introduction Feature selection is a crucial step in machine learning that involves selecting the most relevant features (variables) for building an efficient and accurate….
Data Normalization and Standardization
Data Normalization and Standardization: A Comprehensive Guide Introduction Data preprocessing is a crucial step in machine learning, and normalization and standardization are two fundamental techniques used to rescale data. These….