我不确定为什么决策树和随机森林显示 100% 的准确率？

我目前正在研究一个模型，该模型可以读取结构化数据并确定某人是否患有疾病。我认为问题在于数据没有在训练数据和测试数据之间分开。我不知道我怎样才能做到这一点。

我不知道该尝试什么。

import pandas as pd
import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
import seaborn as sns

from sklearn.tree import DecisionTreeClassifier



heart_data = pd.read_csv('cardio_train.csv')

heart_data.head()

heart_data.shape

heart_data.describe()

heart_data.isnull().sum()

heart_data_columns = heart_data.columns

predictors = heart_data[heart_data_columns[heart_data_columns != 'target']]  # all columns except Breast Cancer
target = heart_data['target']  # Breast Cancer column


#This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type
predictors.head()
target.head()

#normalize the data by subtracting the mean and dividing by the standard deviation.

predictors_norm = (predictors - predictors.mean()) / predictors.std()
predictors_norm.head()



n_cols = predictors_norm.shape[1]  # number of predictors


def regression_model():
    # create model
    model = Sequential()
    #inputs
    model.add(Dense(50, activation='relu', input_shape=(n_cols,)))
    model.add(Dense(50, activation='relu')) # activation function
    model.add(Dense(1))

    # compile model
    model.compile(optimizer='adam', loss='mean_squared_error')
    #loss measures the results and figures out how bad it did. Optimizer generates next guess.
    return model


# build the model
model = regression_model()
print (model)
# fit the model
history=model.fit(predictors_norm, target, validation_split=0.3, epochs=10, verbose=2)


#Decision Tree
print ("Processing Decision Tree")
dtc = DecisionTreeClassifier()
dtc.fit(predictors_norm,target)
print("Decision Tree Test Accuracy {:.2f}%".format(dtc.score(predictors_norm, target)*100))


#Support Vector Machine
print ("Processing Support Vector Machine")
svm = SVC(random_state = 1)
svm.fit(predictors_norm, target)
print("Test Accuracy of SVM Algorithm: {:.2f}%".format(svm.score(predictors_norm,target)*100))

#Random Forest
print ("Processing Random Forest")
rf = RandomForestClassifier(n_estimators = 1000, random_state = 1)
rf.fit(predictors_norm, target)
print("Random Forest Algorithm Accuracy Score : {:.2f}%".format(rf.score(predictors_norm,target)*100))

我收到的消息是这样的 决策树测试准确率 100.00% 然而，支持向量机获得了 73.37%

您正在使用与训练模型时相同的数据来评估您的模型：您可能是过拟合。为了克服这个问题，您必须将数据分为两部分，一部分用于学习，一部分用于测试：

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(predictors, target, test_size=0.2)

然后，使用训练数据集学习您的模型并在测试数据集上对其进行评估：

dtc = DecisionTreeClassifier()
dtc.fit(x_train, y_train)
accuracy = dtc.score(x_test, y_test) * 100
print(f"Decision Tree test accuracy : {accuracy} %.")