from sklearn.linear_model
import Ridge
from sklearn.preprocessing
import StandardScaler
from sklearn.svm
import SVC
from sklearn.tree
import DecisionTreeClassifier, export_graphviz
from IPython.display
import display
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mt
import pandas as pd
from sklearn.decomposition
import PCA
from sklearn.datasets
import load_breast_cancer
from sklearn.model_selection
import train_test_split
rnd =
np.random.RandomState(0)
X_org = rnd.normal(size=(1000, 3
))
w = rnd.normal(size=3
)
X = rnd.poisson(10 *
np.exp(X_org))
y =
np.dot(X_org, w)
print(
"Number of feature appearances:\n{}".format(np.bincount(X[:, 0])))
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=
0)
#岭回归验证测试分数
score =
Ridge().fit(X_train, y_train).score(X_test, y_test)
print(
"Ridge Test score: {:.3f}".format(score))
X_train_log = np.log(X_train + 1
)
X_test_log = np.log(X_test + 1
)
score =
Ridge().fit(X_train_log, y_train).score(X_test_log, y_test)
print(
"Test score: {:.3f}".format(score))
Ridge Test score: 0.622Test score: 0.875
用log变换一般是在连续值拉锯越来越大时使用。
