Principal Component Analysis (PCA) is a widely used dimensionality reduction technique in data science and machine learning. It helps to transform high-dimensional data into a lower-dimensional form while retaining as much variance as possible. But theory alone doesn’t make a technique useful. To fully appreciate PCA, it’s helpful to explore real-world principal component analysis examples … Read more
Principal Component Analysis (PCA) is a popular technique used in data science and machine learning for dimensionality reduction. However, many beginners and even experienced practitioners often ask the question: Is PCA machine learning? The answer depends on how we define machine learning and whether PCA fits into that definition. In this article, we will explore … Read more
Principal Component Analysis (PCA) is a widely used dimensionality reduction technique in machine learning and data science. It helps simplify complex datasets while preserving as much variance as possible. By reducing the number of features, PCA improves computational efficiency, reduces overfitting, and enhances model performance. In this article, we will explain how to implement PCA … Read more
In the world of machine learning and statistics, handling multicollinearity and high-dimensional data can pose significant challenges. Principal Component Regression (PCR) is a technique that combines the dimensionality reduction power of Principal Component Analysis (PCA) with regression analysis to address these challenges effectively. In this comprehensive guide, we will delve into what principal component regression … Read more
One of the most important factors that affect the model performance lies within feature engineering in the domain of machine learning. Data scientists and ML engineers go through trials and errors to refine these data points into meaningful features that fuel the predictive power of their models. From the selection of the most relevant features … Read more
