Python uses Statsmodes for Kernel density estimation, Quantile regression, regression tree, etc

To use Statsmodes in Python for Kernel density estimation, Quantile regression and regression tree analysis, you need to ensure that Statsmodes and related class libraries have been installed. The following steps can be used to prepare for environment setup: 1. Install Statsmodes library: pip install statsmodels 2. Install dependent class libraries: -NumPy: Used to handle numerical calculations. -Pandas: used for data processing and analysis. -SciPy: used for scientific calculations and statistics. -Matplotlib: used for drawing and Data and information visualization. You can install these libraries using the following command: pip install numpy pandas scipy matplotlib Next, we will introduce examples of Kernel density estimation, Quantile regression and regression tree using Statsmodes, and provide a dataset for experiments. **Dataset introduction:** To demonstrate Kernel density estimation and Quantile regression, we will use the 'star98' data set that comes with Statsmodes. This dataset is a virtual dataset that contains various variables used to predict school comprehensive scores. **Dataset download website:** You can download the 'star98' dataset through the following code: python import statsmodels.api as sm data = sm.datasets.get_rdataset('star98').data **Sample code:** The following is a complete sample code including Kernel density estimation, Quantile regression and regression tree: python import statsmodels.api as sm import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy import stats #Import the star98 dataset data = sm.datasets.get_rdataset('star98').data #Kernel density estimation density = sm.nonparametric.KDEUnivariate(data['pctymle']) density.fit() #Draw Kernel density estimation chart plt.plot(density.support, density.density) plt.xlabel('Percentage of young male with low earnings') plt.ylabel('Density') plt.show() #Quantile regression quantiles = np.arange(0.1, 1, 0.1) mod = sm.quantreg('np.log(r1) ~ np.log(pctymle)', data) res = mod.fit(q=quantiles) #Output regression results print(res.summary()) #Regression tree from sklearn.tree import DecisionTreeRegressor #Prepare data X = data[['pctymle', 'st_ratio']] y = data['r1'] #Fitting regression Tree model model = DecisionTreeRegressor(max_depth=2) model.fit(X, y) #Draw Regression Tree from sklearn import tree plt.figure(figsize=[10, 5]) _ = tree.plot_tree(model, filled=True, feature_names=['pctymle', 'st_ratio']) plt.show() The above code implements examples of Kernel density estimation, Quantile regression and regression tree using Statsmodes. You can modify the dataset and related parameters according to your own needs.