python - 我不确定为什么决策树和随机森林显示 100% 准确度？

Question

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进行拆分。我不知道我将如何做到这一点。

我不确定要尝试什么。

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))

我得到的消息是这个决策树测试准确度 100.00% 但是，支持向量机得到 73.37%

标签： pythonkerasscikit-learn