Sklearn预测python3.5

我正在使用sklearn LogisticRegression训练Logistic回归模型。当试图预测测试集时，我得到一个TypeError。Sklearn预测python3.5

CODE：

test_features=test[["Sex","Age","Pclass","Fare","Embarked"]].values 
myprediction=myfit2.predict(test_features)

错误：

float() argument must be a string or a number

我检查语法几个times.Could这是因为我使用Python 3.5。因为这似乎在python 2.7上工作得很好。将不胜感激帮助解决这个错误。

来源

2017-07-07 Pooja Ravichandran

可你上传的测试数据集？另外，你在这个测试数据集中是否有NaN或字符串？ – sera

https://www.kaggle.com/c/titanic/data我已经推断所有NaN，并用字符串替换所有分类变量，然后将其提供给预测函数 –

问题是，您使用“性别”包含字符串而不是数字。 predict（）函数仅将输入数据集作为输入。你需要将“性别”转换为男性为1和男性为0的二进制 – sera

的问题是，该数据包含楠：

代码：

import pandas as pd 
from numpy import nanmean 
import numpy as np 
from sklearn.linear_model import LogisticRegression 

train = pd.read_csv("train.csv") 
test = pd.read_csv("test.csv") 

test["Sex"][test["Sex"]=="male"]=0 
test["Sex"][test["Sex"]=="female"]=1 
test["Embarked"][test["Embarked"]=='S']=0 
test["Embarked"][test["Embarked"]=='C']=1 
test["Embarked"][test["Embarked"]=='Q']=2 

train["Sex"][train["Sex"]=="male"]=0 
train["Sex"][train["Sex"]=="female"]=1 
train["Embarked"][train["Embarked"]=='S']=0 
train["Embarked"][train["Embarked"]=='C']=1 
train["Embarked"][train["Embarked"]=='Q']=2 

nan_mean_age = nanmean(test.iloc[:,4]) 
test = test.fillna(value = nan_mean_age) 

nan_mean_age2 = nanmean(train.iloc[:,5]) 
train = train.fillna(value = nan_mean_age2) 

train_features=train[["Sex","Age","Pclass","Fare","Embarked"]].values 
test_features=test[["Sex","Age","Pclass","Fare","Embarked"]].values 

train_features = np.asarray(train_features) 
test_features = np.asarray(test_features) 

lg = LogisticRegression() 
#define your target variable y and then fit 
y_train = train.iloc[:,1] 

lg.fit(train_features,y_train) 
lg.predict(test_features)

结果：

array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 
     1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 
     0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 
     1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 
     1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 
     0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
     1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 
     0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 
     1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 
     0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 
     0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 
     0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 
     0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 
     0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 
     0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
     1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 
     1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 
     1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 
     1, 0, 0, 0], dtype=int64)

像这样的东西应该工作正常

来源

2017-07-07 11:18:00 sera

已经做到了第一次**代码：** test [“Sex”] [test [“Sex”] ==“male”] = 0 test [“性别”] [测试[“性别”] ==“女性”] = 1 测试[“进入”] [测试[“进入”] =='S'] = 0 test [登入“] [test [”登入“] =='C'] = 1 test [”登上“] [测试[”登上“] =='Q'] = 2火车组也一样 –

@PoojaRavichandran看到我的新解决方案。您需要更换Nan – sera

我确实将年龄的平均值代替了所有的NaN。 –

Sklearn预测python3.5

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