Bayesian Optimization
Bayesian Optimization: A Comprehensive Guide Introduction Bayesian Optimization (BO) is an efficient method for optimizing black-box functions that are expensive to evaluate. It is widely used in hyperparameter tuning for….
Tag: Statistical Learning
Autoregressive Models
Autoregressive (AR) Models: A Comprehensive Guide 1. Introduction to Autoregressive (AR) Models Autoregressive (AR) models are one of the fundamental models used in time series forecasting. The AR model predicts….
t-SNE for High-Dimensional Data
t-SNE (t-Distributed Stochastic Neighbor Embedding) is a powerful technique for dimensionality reduction and visualization of high-dimensional data. Unlike PCA, which is a linear method, t-SNE is a non-linear technique that….
K-Means Clustering
K-Means Clustering: A Comprehensive Guide 1. Introduction to K-Means Clustering K-Means Clustering is an unsupervised machine learning algorithm used for grouping similar data points into clusters. It aims to partition….
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….
Polynomial Regression
Polynomial Regression in Machine Learning 1. Introduction to Polynomial Regression Polynomial Regression is an extension of Linear Regression that models the relationship between the independent variable (X) and the dependent….
Linear Regression
Linear Regression in Machine Learning 1. Introduction to Linear Regression Linear Regression is one of the most fundamental and widely used supervised learning algorithms in machine learning. It is primarily….
Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)
Model Evaluation Metrics in Machine Learning Evaluating a machine learning model is crucial for ensuring its effectiveness. Model evaluation metrics provide a way to measure performance, compare models, and fine-tune….
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….