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XGBoost is an excellent algorithm that has been widely used in many competitions such as Kaggle, where we can see that many winning teams utilize XGBoost and achieve outstanding performance. Currently, most classification models in practical applications are based on XGBoost, making it a very practical and user-friendly algorithm. The main reference for this article … 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
Source: I learned at Xuecheng This article is about 3400 words and is recommended to read in 5 minutes. It evaluates the performance of models from the perspectives of speed and accuracy. Boosting algorithms are a class of machine learning algorithms that build a strong classifier by iteratively training a series of weak classifiers (usually … Read more
Extreme Gradient Boosting (XGBoost) is an open-source library that provides an efficient implementation of the gradient boosting algorithm. Although other open-source implementations of this method existed before XGBoost, the release of XGBoost seems to have unleashed the power of the technique and brought gradient boosting to the attention of the machine learning community at large. … Read more
Using the XGBoost algorithm often yields good results in Kaggle and other data science competitions, which has made it popular. This article analyzes the prediction process of the XGBoost machine learning model using a specific dataset, and by employing visualization techniques to display the results, we can better understand the model’s prediction process. As the … 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
Author: Jason Brownlee Translation: wwl Proofreader: Wang Yutong This article contains about 3300 words, and is recommended to be read in 10minutes This article introduces how to use XGBoost for time series forecasting, including transforming time series into a supervised learning prediction problem, using forward validation for model evaluation, and providing actionable code examples. For … 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
Click on the “MLNLP” above to select the “Star” public account Heavyweight content delivered to you first This article is the third in a series on decision trees, mainly introducing mainstream ensemble algorithms based on the Boosting framework, including XGBoost and LightGBM. Here is the complete mind map: XGBoost XGBoost is a tool for large-scale … Read more
A Powerful Tool in Kaggle: The XGBoost Algorithm Hi, everyone, I am Chenxi. This article is also based on a request from a friend who has been pushed by their boss to build a model, so they reached out to me for some guidance. Student A: Chenxi, Ionly know R, is there a model that … Read more