Python 导入CSV，重塑变量'；logistic回归的s数组
我希望大家在新冠肺炎大流行期间都能保持安全。我是Python新手，有一个关于将数据从CSV导入Python的快速问题，用于进行简单的逻辑回归分析，其中因变量是二进制的，自变量是连续的 我导入了一个CSV文件，然后希望使用一个变量（活动）作为自变量，另一个变量（烟雾）作为响应变量。我可以将CSV文件加载到Python中，但每次我尝试生成逻辑回归模型来预测运动产生的烟雾时，我都会得到一个错误，即运动必须重新设置为一列（二维），因为它当前是一维的Python 导入CSV，重塑变量'；logistic回归的s数组,python,numpy,statistics,regression,reshape,Python,Numpy,Statistics,Regression,Reshape,我希望大家在新冠肺炎大流行期间都能保持安全。我是Python新手，有一个关于将数据从CSV导入Python的快速问题，用于进行简单的逻辑回归分析，其中因变量是二进制的，自变量是连续的 我导入了一个CSV文件，然后希望使用一个变量（活动）作为自变量，另一个变量（烟雾）作为响应变量。我可以将CSV文件加载到Python中，但每次我尝试生成逻辑回归模型来预测运动产生的烟雾时，我都会得到一个错误，即运动必须重新设置为一列（二维），因为它当前是一维的 import matplotlib.pyplot as
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
data = pd.read_csv('Pulse.csv') # Read the data from the CSV file
x = data['Active'] # Load the values from Exercise into the independent variable
x = np.array.reshape(-1,1)
y = data['Smoke'] # The dependent variable is set as Smoke
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
data = pd.read_csv('Pulse.csv') # Read the data from the CSV file
x = data['Active'] # Load the values from Exercise into the independent variable
y = data['Smoke'] # The dependent variable is set as Smoke
lr = LogisticRegression().fit(x.values.reshape(-1,1), y)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
data = pd.read_csv('Pulse.csv') # Read the data from the CSV file
x = data['Active'] # Load the values from Exercise into the independent variable
y = data['Smoke'] # The dependent variable is set as Smoke
lr = LogisticRegression().fit(x.values.reshape(-1,1), y)
下面的代码应该可以工作：
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
data = pd.read_csv('Pulse.csv')
x = pd.DataFrame(data['Smoke'])
y = data['Smoke']
lr = LogisticRegression()
lr.fit(x,y)
p_pred = lr.predict_proba(x)
y_pred = lr.predict(x)
score_ = lr.score(x,y)
conf_m = confusion_matrix(y,y_pred)
report = classification_report(y,y_pred)
print(score_)
0.8836206896551724
print(conf_m)
[[204 2]
[ 25 1]]
下面的代码应该可以工作：
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
data = pd.read_csv('Pulse.csv')
x = pd.DataFrame(data['Smoke'])
y = data['Smoke']
lr = LogisticRegression()
lr.fit(x,y)
p_pred = lr.predict_proba(x)
y_pred = lr.predict(x)
score_ = lr.score(x,y)
conf_m = confusion_matrix(y,y_pred)
report = classification_report(y,y_pred)
print(score_)
0.8836206896551724
print(conf_m)
[[204 2]
[ 25 1]]
请不要使用这一行
x=np.array.reformate（-1,1）x=np.array.reformate（-1,1）p_pred=lr.predict_proba（x）y_pred=lr.predict（x）score_u=lr.score（x，y）conf_m=conflusion_matrix（y，y_pred）report=classification_report（y，y_pred）p_pred=lr.predict_proba（x）y_pred=lr.predict（x）score_u=lr.score（x，y）conf_m=conflusion_matrix（y，y_pred）report=classification_report（y，y_pred）x=pd.DataFrame（data['Smoke'））x系列数据帧