下載地址：https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data

打開方式： 直接用excel可以打開，轉(zhuǎn)存為csv等

模型訓練

from sklearn.linear_model import Lasso from sklearn.preprocessing import StandardScaler from sklearn.datasets import load_bostonboston = load_boston() scaler = StandardScaler()X = scaler.fit_transform(boston["data"]) print('X',X) Y = boston["target"] print('Y', Y) names = boston["feature_names"] print('names',names)

LASSO懲罰回歸

from sklearn.linear_model import Lasso from sklearn.preprocessing import StandardScaler from sklearn.datasets import load_boston import numpy as np#輸出線性方程的輔助函數(shù) def pretty_print_linear(coefs, names = None, sort = False):if names is None:names = ["X%s" % x for x in range(len(coefs))]lst = zip(coefs, names)if sort:lst = sorted(lst, key = lambda x:-np.abs(x[0]))return " + ".join("%s * %s" % (np.round(coef, 3), name) for coef, name in lst) boston = load_boston() scaler = StandardScaler() X = scaler.fit_transform(boston["data"]) Y = boston["target"] names = boston["feature_names"]lasso = Lasso(alpha=.3) lasso.fit(X, Y)print("lasso model:", pretty_print_linear(lasso.coef_, names, sort = True))

總結(jié)

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