python - 用Python检查决策树分类器的准确性

Question

我写了一个函数，它接受数据集(excel/pandas)和一些值，然后用决策树分类器预测结果。我已经用 sklearn 做到了。 你能帮我解决这个问题吗，我浏览了网络和这个网站，但找不到有效的答案。 我试过这样做，但它不起作用:

from sklearn.metrics import accuracy_score
score = accuracy_score(variable_list, result_list)

这是我得到的错误:

ValueError: Classification metrics can't handle a mix of continuous-multioutput and multiclass targets

这是代码(为了准确性我删除了代码)

import pandas as pd
import math
import xlrd
from sklearn.model_selection import train_test_split
from sklearn import tree

def predict_concrete_class(input_data, cement, blast_fur_slug,fly_ash,
                            water, superpl, coarse_aggr, fine_aggr, days):

    data_for_tree = concrete_strenght_class(input_data)

    variable_list = []
    result_list = []

    for index, row in data_for_tree.iterrows():
        variable = row.tolist()
        variable = variable[0:8]

        variable_list.append(variable)

        result_list.append(row[-1])

    decision_tree = tree.DecisionTreeClassifier()
    decision_tree = decision_tree.fit(variable_list,result_list)

    input_values = [cement, blast_fur_slug, fly_ash, water, superpl, coarse_aggr, fine_aggr, days]

    prediction = decision_tree.predict([input_values])

    info = "Prediction of future concrete class after "+ str(days)+" days: "+ str(prediction[0])

    return info

print(predict_concrete_class(data, 500, 0, 0, 200, 0, 1125, 613, 3))

Answer 1

1. 将您的数据拆分为训练和测试:

   var_train, var_test, res_train, res_test = train_test_split(variable_list, result_list, test_size = 0.3)
   

2. 在训练集上训练您的决策树:

   decision_tree = tree.DecisionTreeClassifier()
   decision_tree = decision_tree.fit(var_train, res_train)
   

3. 通过计算测试集的准确度来测试模型性能:

   res_pred = decision_tree.predict(var_test)
   score = accuracy_score(res_test, res_pred)
   

   或者你可以直接使用decision_tree.score:

   score = decision_tree.score(var_test, res_test)
   

您收到的错误是因为您试图将 variable_list(这是您的输入特征列表)作为 accuracy_score 中的参数传递。您应该传递真实标签和预测标签列表。