The Knowledge Graph is an important branch of artificial intelligence. It was proposed by Google in 2012 and has become a killer application for building large-scale knowledge, playing a crucial role in areas such as search, natural language processing, intelligent assistants, and e-commerce. The Knowledge Graph, along with big data and deep learning, has become … Read more
1. Author Information Authors: Shao Bilin, Ren Meng, Tian Ning Affiliation: School of Management, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710311 Author Biography: Shao Bilin (1965-), male, professor, master’s supervisor, doctoral supervisor, research interests include big data, artificial intelligence, data information and management, energy sustainable development. E-mail: [email protected] Corresponding Author: Ren Meng (1999-), … Read more
1 Algorithm Introduction LightGBM (Light Gradient Boosting Machine, hereinafter referred to as LGBM) is an efficient and scalable machine learning algorithm based on Gradient Boosted Decision Trees (GBDT). As a member of the GBDT framework algorithms and a successor to the XGB algorithm, LGBM effectively integrates a series of advantages from previous GBDT algorithms, including … Read more
Click the blue text to follow us 2023, Issue 2 Nutritional Component Analysis and Hypertension Prediction Based on XGBoost Model Jiang Huai, Tan Lang, Li Shijie, Liu Yu, Wang Junfeng Abstract: Hypertension is a common chronic disease, and early detection and intervention can reduce the risk of complications. Although the onset and development of hypertension … Read more
For most data analysts, regression models are undoubtedly one of the fundamental skills. At the beginning of this century, many investment banks used the application of regression models as a standard for evaluating analysts. For over a decade, regression models have maintained a significant presence in all analyses and predictions. However, in recent years, a … Read more
Previous Review Recommended Article: Complexity of Xi’an Approach Airspace Based on Aircraft and Route Network Recommended Article: Lightweight Massive Spatiotemporal Data Processing and Analysis Service Framework This article was published in “Command Information Systems and Technology”, 2022, Issue 5 Authors:Sui Yuan,Duan Ran,Bai Zheng Citation Format:Sui Yuan, Duan Ran, Bai Zheng. Monitoring Method for Ship Imitation … Read more
With the continuous development of genetics, breeding, and the increasing advancements in the Human Genome Project and molecular biology, biological data has experienced explosive growth over just a few decades. For example, algorithms such as regression analysis, random forests, and support vector machines in bioinformatics are already quite mature. Recently, while reading literature, I came … Read more
Case Introduction This case aims to predict the Boston housing data using the XGBoost algorithm. The Boston housing dataset is a commonly used dataset for house price prediction, containing 506 samples and 13 features, including crime rates in the area, average number of rooms per dwelling, and distance to the city center. Algorithm Principle XGBoost … Read more
In recent years, China has made significant improvements in food quality and safety management. However, with the expansion of the food industry and the increasing demand for inspections, food safety testing data has exhibited high-dimensional, complex, and nonlinear characteristics. These features lead to low data utilization in quantitative analysis, directly affecting the accuracy of risk … Read more
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import xgboost as xgb from sklearn.metrics import mean_squared_error color_pal = sns.color_palette() plt.style.use('fivethirtyeight') df = pd.read_csv('AAPL Hourly.csv') df = df[['timestamp', 'close']] df = df.set_index('timestamp') df.plot(style='.', figsize=(15, 5), color=color_pal[0], title='stock growth') plt.show() df.head() mean = df['close'].mean() mean 162.3033402402023 df.index = pd.to_datetime(df.index) … Read more