使用对数变换来提升单变量的回归准确度

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:
{}".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: 0.622
Test score: 0.875

用log变换一般是在连续值拉锯越来越大时使用。