保留集的问题

Python 中的模型验证

Kasey Jones

Data Scientist

过渡到验证集

传统的训练/测试划分：大部分数据用于训练，较小部分仅用于测试。

X_train, X_val, y_train, y_val =
    train_test_split(X, y,
    test_size=0.2)

rf = RandomForestRegressor()

rf.fit(X_train, y_train)

out_of_sample = rf.predict(X_test)
print(mae(y_test, out_of_sample))

10.24

传统训练划分

cd = pd.read_csv("candy-data.csv")
s1 = cd.sample(60, random_state=1111)
s2 = cd.sample(60, random_state=1112)

重叠的糖果数：

print(len([i for i in s1.index if i in s2.index]))

传统训练划分

巧克力糖果数：

print(s1.chocolate.value_counts()[0])
print(s2.chocolate.value_counts()[0])

34
30

划分方式很重要

样本 1 测试误差

print('Testing error: {0:.2f}'.format(mae(s1_y_test, rfr.predict(s1_X_test))))

10.32

样本 2 测试误差

print('Testing error: {0:.2f}'.format(mae(s2_y_test, rfr.predict(s2_X_test))))

11.56

训练、验证、测试

X_temp, X_val, y_temp, y_val = train_test_split(..., random_state=1111)
X_train, X_test, y_train, y_test = train_test_split(..., random_state=1111)

rfr = RandomForestRegressor(n_estimators=25, random_state=1111, max_features=4)
rfr.fit(X_train, y_train)

print('Validation error: {0:.2f}'.format(mae(y_test, rfr.predict(X_test))))

9.18

print('Testing error: {0:.2f}'.format(mae(y_val, rfr.predict(X_val))))

8.98

第 2 轮

X_temp, X_val, y_temp, y_val = train_test_split(..., random_state=1171)
X_train, X_test, y_train, y_test = train_test_split(..., random_state=1171)

rfr = RandomForestRegressor(n_estimators=25, random_state=1111, max_features=4)
rfr.fit(X_train, y_train)

print('Validation error: {0:.2f}'.format(mae(y_test, rfr.predict(X_test))))

8.73

print('Testing error: {0:.2f}'.format(mae(y_val, rfr.predict(X_val))))

10.91

保留集练习

Python 中的模型验证