数据集划分以及交叉验证法_数据集的划分及交叉验证

数据集划分

训练集 验证集 测试集

• 训练集用来构建模型
• 验证集用来在构建过程中微调模型，辅助模型构建，可以重复使用。（当有验证集存在时，我们从训练集选一部分用于测试模型，保留一部分训练集数据作为验证集）
• 测试集用于检验模型，评估模型的准确率。

Hold-out method

默认将数据集的75%作为训练集，数据集的25%作为测试集。

交叉验证

1.留一验证法

把一个大的数据集分为k个小数据集，k等于数据集中数据的个数，每次只使用一个作为测试集，剩下的全部作为训练集，这种方法得出的结果与训练整个测试集的期望值最为接近，但是成本过于庞大，适合小样本数据集。

2. K折交叉验证

将数据集分成k个子集，每次选k-1个子集作为训练集，剩下的那个子集作为测试集。一共进行k次，将k次的平均交叉验证正确率作为结果。train_test_split，默认训练集、测试集比例为3:1。如果是5折交叉验证，训练集比测试集为4:1；10折交叉验证训练集比测试集为9:1。数据量越大，模型准确率越高。

from sklearn.model_selection import KFold 
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn import metrics

data = load_iris() #获取鸢尾花数据集
X = data.data
y = data.target
kf = KFold(n_splits=5, random_state=None) # 5折交叉验证

i = 1
for train_index, test_index in kf.split(X, y):
    print('\n{} of kfold {}'.format(i,kf.n_splits))
    X_train, X_test = X[train_index], X[test_index] 
    y_train, y_test = y[train_index], y[test_index]
    model = LogisticRegression(random_state=1)
    model.fit(X_train, y_train)
    pred_test = model.predict(X_test)
    score = metrics.accuracy_score(y_test, pred_test)
    print('accuracy_score', score)
    i += 1
    pred = model.predict_proba(X_test)[:, 1]

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3. 分层交叉验证

分层是重新将数据排列组合，使得每一折都能比较好地代表整体。

在一个二分类问题上，原始数据一共有两类（F和M），F：M的数据量比例大概是 1:3；划分了5折，每一折中F和M的比例都保持和原数据一致（1:3）。

from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris

data = load_iris()
X = data.data
y = data.target
skf = StratifiedKFold(n_splits=5,shuffle=True,random_state=0)

for train_index, test_index in skf.split(X,y): 
    print("Train:", train_index, "Validation:", test_index) 
    X_train, X_test = X[train_index], X[test_index] 
    y_train, y_test = y[train_index], y[test_index]
    
model = LogisticRegression()
scores = cross_val_score(model,X,y,cv=skf)
print("straitified cross validation scores:{}".format(scores))
print("Mean score of straitified cross validation:{:.2f}".format(scores.mean()))

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4. 重复交叉验证

其实就是重复n次k-fold，每次重复有不同的随机性。

from sklearn.model_selection import RepeatedKFold
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn import metrics

data = load_iris()
X = data.data
y = data.target

kf = RepeatedKFold(n_splits=5, n_repeats=2, random_state=None) 

for train_index, test_index in kf.split(X):
      print("Train:", train_index, "Validation:",test_index)
      X_train, X_test = X[train_index], X[test_index] 
      y_train, y_test = y[train_index], y[test_index]
        
i = 1
for train_index, test_index in kf.split(X, y):
    print('\n{} of kfold {}'.format(i,i))
    X_train, X_test = X[train_index], X[test_index] 
    y_train, y_test = y[train_index], y[test_index]
    model = LogisticRegression(random_state=1)
    model.fit(X_train, y_train)
    pred_test = model.predict(X_test)
    score = metrics.accuracy_score(y_test, pred_test)
    print('accuracy_score', score)
    i += 1
    #pred_test = model.predict(X_test)
    pred = model.predict_proba(X_test)[:, 1]

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