<-- home

Why Train Test Split

April 22, 2019

为什么要训练数据集和测试数据集

模型的泛化能力

  • 泛化能力即由此及彼的能力
  • 模型在面对新的数据样本时的预测能力
  • 过拟合、欠拟合泛化能力差

如何提高模型的泛化能力

  • 训练数据集、测试数据集分离
  • 如果使用训练数据集得到的模型对测试数据集预测能力较好的话，我们就说模型泛化能力不错
  • 如果模型泛化能力差，多半可能遭遇了过拟合
import numpy as np
from sklearn.model_selection import train_test_split

np.random.seed(666)
x = np.random.uniform(-3, 3, size=100)
X = x.reshape(-1, 1)
y = 0.5 * x**2 + x + 2 + np.random.normal(0, 1, size=100)

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

def PolynomialRegression(degree):
    return Pipeline([
        ("poly", PolynomialFeatures(degree)),
        ("std_scaler", StandardScaler()),
        ("lin_reg", LinearRegression())
    ])

线性回归

lin_reg = LinearRegression()
lin_reg.fit(X_train, y_train)
y_predict = lin_reg.predict(X_test)
lin_reg.score(X_test, y_test)

0.5437074558433677

mean_squared_error(y_test, y_predict)

2.2199965269396573

多项式回归

degree=2

poly2_reg = PolynomialRegression(2)
poly2_reg.fit(X_train, y_train)
y_predict_poly2 = poly2_reg.predict(X_test)
poly2_reg.score(X_test, y_test)

0.8348374397431063

mean_squared_error(y_test, y_predict_poly2)

0.8035641056297901

degree=10

poly10_reg = PolynomialRegression(10)
poly10_reg.fit(X_train, y_train)
y_predict_poly10 = poly10_reg.predict(X_test)
poly10_reg.score(X_test, y_test)

0.8106397218492938

mean_squared_error(y_test, y_predict_poly10)

0.9212930722150721

degree=100

poly100_reg = PolynomialRegression(100)
poly100_reg.fit(X_train, y_train)
y_predict_poly100 = poly100_reg.predict(X_test)
poly100_reg.score(X_test, y_test)

-2893100737.14305

mean_squared_error(y_test, y_predict_poly100)

14075780270.824253