我正在嘗試使用 scikit_learn 和 pandas 解決 python 中的決策樹問題。數據集以 CSV 文件的形式提供。當我嘗試在 python 中加載數據時，我收到一條錯誤消息，顯示“ValueError：無法將字符串轉換為浮點數：'CustomerID'”。我不知道我在代碼中做錯了什么。import pandas as pdfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn import metricscol_names=['CustomerID','Gender','Car Type', 'Shirt Size','Class']pima=pd.read_csv("F:\Current semster courses\Machine Learning\ML_A1_Fall2019\Q2_dataset.csv",header=None, names=col_names)pima.head()feature_cols=['CustomerID','Gender','Car Type', 'Shirt Size']X=pima[feature_cols]y=pima.ClassX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)clf = DecisionTreeClassifier()# Train Decision Tree Classiferclf = clf.fit(X_train,y_train)#Predict the response for test datasety_pred = clf.predict(X_test)print("Accuracy:",metrics.accuracy_score(y_test, y_pred))有人可以告訴我我做錯了什么嗎？數據集：CustomerID Gender Car Type Shirt Size Class1 M Family Small C02 M Sports Medium C03 M Sports Medium C04 M Sports Large C05 M Sports Extra Large C06 M Sports Extra Large C07 F Sports Small C08 F Sports Small C09 F Sports Medium C010 F Luxury Large C011 M Family Large C112 M Family Extra Large C113 M Family Medium C114 M Luxury Extra Large C115 F Luxury Small C116 F Luxury Small C117 F Luxury Medium C118 F Luxury Medium C119 F Luxury Medium C120 F Luxury Large C1
1 回答
眼眸繁星
TA貢獻1873條經驗 獲得超9個贊
啊。好的。問題是您的數據是分類數據，scikit不能直接使用。它首先需要轉換為數字數據。該方法._get_dummies()通過獲取具有多個分類值的單列并將其轉換為多列，每列包含一個數字 1 或 0，指示哪個類別是否為“真”。
順便說一句，您應該從功能中刪除“客戶 ID”列。它是一個隨機值，與該行是否屬于一個類別無關。
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics
col_names=['CustomerID','Gender','Car Type', 'Shirt Size','Class']
data = [['1', 'M', 'Family', 'Small', 'C0'],
['2', 'M', 'Sports', 'Medium', 'C0'],
['3', 'M', 'Sports', 'Medium', 'C0'],
['4', 'M', 'Sports', 'Large', 'C0'],
['5', 'M', 'Sports', 'Extra Large','C0'],
['6', 'M', 'Sports', 'Extra Large','C0'],
['7', 'F', 'Sports', 'Small', 'C0'],
['8', 'F', 'Sports', 'Small', 'C0'],
['9', 'F', 'Sports', 'Medium', 'C0'],
['10', 'F', 'Luxury', 'Large', 'C0'],
['11', 'M', 'Family', 'Large', 'C1'],
['12', 'M', 'Family', 'Extra Large','C1'],
['13', 'M', 'Family', 'Medium', 'C1'],
['14', 'M', 'Luxury', 'Extra Large','C1'],
['15', 'F', 'Luxury', 'Small', 'C1']]
#pima=pd.read_csv("F:\Current semster courses\Machine ...
pima=pd.DataFrame(data, columns = col_names)
# Convert the categorical data to multiple columns of numerical data for the decision tree
pima = pd.get_dummies(pima, prefix=['CustomerID','Gender','Car Type', 'Shirt Size','Class'])
print(pima)
#feature_cols=['CustomerID','Gender','Car Type','Shirt Size']
feature_cols=['Gender_F', 'Gender_M',
'Car Type_Family', 'Car Type_Luxury', 'Car Type_Sports',
'Shirt Size_Extra Large', 'Shirt Size_Large', 'Shirt Size_Medium',
'Shirt Size_Small', 'Class_C0', 'Class_C1']
X=pima[feature_cols]
y=pima[['Class_C0', 'Class_C1']]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
print("X_train =", X_train)
print("X_test =", X_test)
print("y_train =", y_train)
print("y_test =", y_test )
clf = DecisionTreeClassifier()
# Train Decision Tree Classifer
clf = clf.fit(X_train,y_train)
#Predict the response for test dataset
y_pred = clf.predict(X_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
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