x 和 y 必须具有相同的第一维，但具有 (4536, 32) 和 (1944, 24) 的形状

Question

我是机器学习领域的初学者，我被困在某个地方。我真的需要一些帮助。我有一个由州名、月份、温度和降雨量组成的数据集。我的代码是：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

data=pd.read_csv('cropdata.csv')
x=data.iloc[:, :-1].values
y=data.iloc[:, 4].values


district = pd.get_dummies(data['District'],drop_first = False)
month = pd.get_dummies(data['Month'],drop_first = False)
crop = pd.get_dummies(data['Crop'],drop_first = False)
data= pd.concat([data,district],axis=1)
data.drop('District', axis=1,inplace=True)
data= pd.concat([data,month],axis=1)
data.drop('Month', axis=1,inplace=True)
data= pd.concat([data,crop],axis=1)
data.drop('Crop', axis=1,inplace=True)

print(data.head(1))

train=data.iloc[:, 0:44].values
test=data.iloc[: ,44:].values

from sklearn.preprocessing import Imputer
imputer1 = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer1 = imputer1.fit(train[:, 0:44])
train[:, 0:44] = imputer1.transform(train[:, 0:44])


from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(train,test,test_size=0.3)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)


from sklearn.tree import DecisionTreeRegressor
from sklearn.datasets import load_iris
clf=DecisionTreeRegressor(max_depth = 19,random_state = None)

#Fitting the classifier into training set
clf.fit(X_train,y_train)
pred=clf.predict(X_test)

print(pred)
predx=pred.round()


from sklearn.metrics import accuracy_score
# Finding the accuracy of the model
a=accuracy_score(y_test,pred.round())
print("The accuracy of this model is: ", a*100)


from sklearn import tree
iris = load_iris()
clf = clf.fit(iris.data, iris.target)
plt.figure(figsize=(10,10))
tree.plot_tree(clf);

模型的准确率为 70%，但有错误：

ValueError: x 和 y 必须具有相同的第一维，但具有形状 (4536, 44) 和 (1944, 12)

现在我不明白我能做些什么来消除错误以及如何根据这个问题绘制图表？

Answer 1

根据您的错误，您的 X_train 数据包含 4536 行用于训练数据集，这意味着每一行都有自己的目标值（标签），因此标签值应为 4536(y_train)

但您的 y_train 仅包含 1944 标签，与 X_train 所需标签不匹配

每个 X_train 需要相应的标签，你提供一些标签，其余的都是无标签的

x 和 y 必须具有相同的第一维，但具有 (4536, 32) 和 (1944, 24) 的形状

x and y must have same first dimension, but have shapes (4536, 32) and (1944, 24)

analysis

machine-learning

decision-tree

spyder