GradientBoosting_regression_model

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn import preprocessing
from sklearn.model_selection import train_test_split,GridSearchCV

from sklearn.linear_model import Lasso,Ridge
from sklearn.ensemble import RandomForestRegressor

from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error

#忽视警告
import warnings
warnings.filterwarnings("ignore")

dataset = datasets.load_boston()
featurenames = list(dataset.feature_names)
X,y = dataset.data,dataset.target

scaler = preprocessing.StandardScaler()
x = scaler.fit_transform(X)

#特征提取
clf = Lasso(alpha=1.0)
clf.fit(x,y)
coefs = clf.coef_
scores = {}
for name,coef in zip(featurenames,coefs):
    sco