StratifiedShuffleSplit中split.split(data,data[:,-1])什么意思
时间: 2024-01-18 18:02:26 浏览: 26
`StratifiedShuffleSplit`是一种交叉验证方法,它可以在保持类别分布不变的情况下,将数据集随机分成训练集和测试集。其中,`split`是一个可迭代的对象,它返回一个生成器,生成器每次迭代返回一个包含训练集和测试集索引的元组。
`split.split(data, data[:,-1])`中的第一个参数`data`是要进行分割的数据集,第二个参数`data[:,-1]`是表示数据集中的类别标签。它们被传入`split`方法中用于生成元组。这里使用`data[:,-1]`作为类别标签是因为它假设类别标签位于数据集的最后一列。在实际使用中,需要根据数据集的具体情况来确定类别标签的位置。
相关问题
import pandas as pd import numpy as np import matplotlib.pyplot as plt import sklearn.model_selection as sM_S import sklearn.preprocessing as sP import sklearn.tree as sT import sklearn.metrics as sM import sklearn.naive_bayes as sNB #1读取数据 data_train = pd.read_excel("train data.xlsx") # 1.1剔除无关列 data = data_train.iloc[:,3:-1] #2预处理 data = data.dropna() #3.数据预处理:空值处理,值映射(分段),归一化/标准化 X = data.iloc[:,0:-1] y = data.iloc[:,-1] mms = sP.MinMaxScaler() X = mms.fit_transform(X) #4.分割数据集和测试集 x_train,x_text,y_train,y_text = sM_S.train_test_split(X,y,test_size=0.33,random_state=42) #5.选择模型 model = sT.DecisionTreeClassifier(max_depth=7) #6.训练模型 model.fit(x_train,y_train) #7.评价模型,赛事要求用F1 y_predict = model.predict(x_text) score = sM.f1_score(y_predict,y_text,average="macro") print("预处理:{} 模型:{} 参数:{} 得分:{}".format("均值填充处理_归一化","决策树","max_depth=7",score)) # 8.应用模型--预测 trainData = pd.read_excel("train data.xlsx") trainData = pd.DataFrame(trainData) trainData = trainData.iloc[:, 3:-2] trainData = trainData.dropna() # 删除空值行 val_data = trainData val_data = mms.fit_transform(val_data) print("预测 train data.xlsx 的结果为:", model.predict(val_data)) 以上这段代码能运行吗?
这段代码存在语法错误,主要是因为第一行导入多个库时没有使用分号或换行符分割。以下是修正后的代码:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn.model_selection as sM_S
import sklearn.preprocessing as sP
import sklearn.tree as sT
import sklearn.metrics as sM
import sklearn.naive_bayes as sNB
# 1.读取数据
data_train = pd.read_excel("train data.xlsx")
# 1.1剔除无关列
data = data_train.iloc[:, 3:-1]
# 2.预处理
data = data.dropna()
# 3.数据预处理:空值处理,值映射(分段),归一化/标准化
X = data.iloc[:, 0:-1]
y = data.iloc[:, -1]
mms = sP.MinMaxScaler()
X = mms.fit_transform(X)
# 4.分割数据集和测试集
x_train, x_test, y_train, y_test = sM_S.train_test_split(X, y, test_size=0.33, random_state=42)
# 5.选择模型
model = sT.DecisionTreeClassifier(max_depth=7)
# 6.训练模型
model.fit(x_train, y_train)
# 7.评价模型,赛事要求用F1
y_predict = model.predict(x_test)
score = sM.f1_score(y_predict, y_test, average="macro")
print("预处理:{} 模型:{} 参数:{} 得分:{}".format("均值填充处理_归一化", "决策树", "max_depth=7", score))
# 8.应用模型--预测
trainData = pd.read_excel("train data.xlsx")
trainData = pd.DataFrame(trainData)
trainData = trainData.iloc[:, 3:-2]
trainData = trainData.dropna() # 删除空值行
val_data = trainData
val_data = mms.fit_transform(val_data)
print("预测 train data.xlsx 的结果为:", model.predict(val_data))
```
请注意,这段代码的运行还需要保证 train data.xlsx 文件存在,并且其中的数据格式与代码中的数据格式一致。
def train(self, train_data): _. y_train, features = train_ data. iloc[:, :-1], train data. iloc[:,-1], train data.colums[:-1]
# Splitting the data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(features, y_train, test_size=0.2, random_state=42)
# Training the model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Evaluating the model on the validation set
y_pred = model.predict(X_val)
accuracy = accuracy_score(y_val, y_pred)
print("Accuracy:", accuracy)
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