如何将我的代码更改为使用k=5的k折叠交叉验证

Question

我想更改我的代码，以代替这部分：

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=100, test_size=0.2)
train_data = X_train.copy()
train_data.loc[:, 'target'] = y_train
test_data = X_test.copy()
test_data.loc[:, 'target'] = y_test

data_config = DataConfig(
target=['target'], #target should always be a list. Multi-targets are only supported for 
regression. Multi-Task Classification is not implemented
continuous_cols=train_data.columns.tolist(),
categorical_cols=[],
normalize_continuous_features=True
)
trainer_config = TrainerConfig(
auto_lr_find=True,
batch_size=64,
max_epochs=10,
)
optimizer_config = {'optimizer':'Adam', 'optimizer_params':{'weight_decay': 0, 'amsgrad': 
False}, 'lr_scheduler':None, 'lr_scheduler_params':{}, 
'lr_scheduler_monitor_metric':'valid_loss'}
model_config = NodeConfig(
task="classification",
num_layers=2,
num_trees=512,
learning_rate=1,
embed_categorical=True,
)
tabular_model = TabularModel(
data_config=data_config,
model_config=model_config,
optimizer_config=optimizer_config,
trainer_config=trainer_config,
)
tabular_model.fit(train=train_data, test=test_data)
pred = tabular_model.predict(test_data)
pred['prediction'] = pred['prediction'].astype(int)
pred.loc[(pred['prediction'] >= 1 )] = 1
print_metrics(test_data['target'], pred["prediction"].astype('int'), tag="Holdout")

我想使用Kfold方法，其中K=5或10。

谢谢你的建议。上面是我使用方法train_testrongplit的完整代码示例。

Answer 1

这里有一个k折叠方法的例子：

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import datasets
from sklearn import svm
X, y = datasets.load_iris(return_X_y=True)
X.shape, y.shape

X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=0)
X_train.shape, y_train.shape
X_test.shape, y_test.shape

clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)
clf.score(X_test, y_test)

结果(在本例中(：

0.9666666666666667

示例如下：https://scikit-learn.org/stable/modules/cross_validation.html