Posts (15)

Principal component analysis (PCA) is a classic multivariate analysis method and a technique based on orthogonal transformation. Reduce the dimension while preserving the feature of the data as much as possible. Often used for data visualization and dealing with multicollinearity issues.

🎵Doofenshmirtz Evil Incorporated!🎵

Magic!

A proof of polynomial differentiation that even a dog can understand.

Show my strongest and brightest side!

A collection of selected works that showcases my achievements in machine learning, programming, and mathematics.

This study will model customer personalities and address common issues in financial data, such as multicollinearity and outliers, by introducing robust logistic regression analysis to predict whether a customer will participate in an event. Due to the imbalance in the predicted target, the commonly used accuracy metric is abandoned in favor of developing a profit-based model evaluation metric to maximize profit as the model selection criterion. Finally, based on principal component selection of variables, the study analyzes and explores the market positioning behind the event and identifies the characteristics of potential customers.

The is about me.

A mathematical model that uses vectors to represent word, allowing computers to calculate the similarity and distance of words. This article will introduce the continuous bag of words (CBOW) and skip-gram based on neural networks.

Introduction the basic model of deep learning neural network, and its important concepts gradient descent and backpropagation.