Question

我正在解决决策树分类问题。代码在下面

import pandas as pd
from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier
from sklearn.model_selection import train_test_split # Import train_test_split function
from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation
col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']
# load dataset
pima = pd.read_csv("diabetes.csv", header=None, names=col_names)
#split dataset in features and target variable
feature_cols = ['pregnant', 'insulin', 'bmi', 'age','glucose','bp','pedigree']
X = pima[feature_cols] # Features
y = pima.label # Target variable
# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test
# Create Decision Tree classifer object
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)

和数据集预览： dataset

我遇到错误

ValueError: could not convert string to float: 'Pregnant'

请帮助我解决此错误。

Answer 1

数据集的第一条非标题行包含看起来重复的标题行。因此，X的第一个值为“怀孕”，而不是您所需要的浮点数。

您可以过滤掉非浮点值或修复数据集。

Answer 2

更改此行以从csv文件读取带有标头的数据：

发件人：

pima = pd.read_csv("diabetes.csv", header=None, names=col_names)

到

pima = pd.read_csv("diabetes.csv") # This will import the data file with the header names from the csv, which you can change later if required.

或使用以下代码手动删除第一行：

pima = pima.iloc[1:]

ValueError：无法将字符串转换为浮点数：“怀孕”

2 个答案: