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python - 为什么 GridSearchCV 方法的准确性低于标准方法？

问题描述

我使用 train_test_split ( random_state = 0) 和没有任何参数调整的决策树来建模我的数据，我运行它大约 50 次以达到最佳精度。

import pandas as pd
import numpy as np

from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

Laptop = pd.ExcelFile(r"D:\Laptop.xlsx",  data_only=True)
data = pd.read_excel(r"D:\Laptop.xlsx",sheet_name=0)

train, test = train_test_split(data, test_size = 0.15)
print("Training size: {}; Test size: {}".format(len(train), len(test)))

c = DecisionTreeClassifier()

features = ["Brand", "Size", "CPU", "RAM", "Resolution", "Class"]

x_train = train[features]
y_train = train["K=20"]
x_test = test[features]
y_test = test["K=20"]

dt = c.fit(x_train, y_train)

y_pred = c.predict(x_test)

from sklearn.metrics import accuracy_score
score = accuracy_score(y_test, y_pred)*100

print ("Accuracy using Decision Tree:", round(score, 1), "%")

在第二步中，我决定使用 GridSearchCV 方法来设置树参数。

import pandas as pd
import numpy as np

from sklearn import tree
from sklearn.model_selection import train_test_split

from matplotlib import pyplot as plt
from scipy.stats import randint
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import RandomizedSearchCV
%matplotlib inline

Laptop = pd.ExcelFile(r"D:\Laptop.xlsx",  data_only=True)
data = pd.read_excel(r"D:\Laptop.xlsx",sheet_name=0)

train, test = train_test_split(data, test_size = 0.15, random_state = 0)
print("Training size: {}; Test size: {}".format(len(train), len(test)))

features = ["Brand", "Size", "CPU", "RAM", "Resolution", "Class"]

x_train = train[features]
y_train = train["K=20"]
x_test = test[features]
y_test = test["K=20"]

from sklearn.model_selection import GridSearchCV

param_dist = {"max_depth":[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
          "min_samples_leaf":randint (10,60)}

tree = DecisionTreeClassifier()
tree_cv = RandomizedSearchCV(tree, param_dist, cv=5)
tree_cv.fit(x_train, y_train)

print("Tuned Decisio Tree Parameters: {}".format(tree_cv.best_params_))
print("Best score is: {}".format(tree_cv.best_score_))

y_pred = tree_cv.predict(x_test)

from sklearn.metrics import accuracy_score
score = accuracy_score(y_test, y_pred)*100

print ("Accuracy using Decision Tree:", round(score, 1), "%")

我在第一种方法中的最佳准确性比 GridSearchCV 方法好得多。

为什么会这样？

你知道以最准确的方式获得最好的树的最佳方法吗？

标签： pythondecision-treegrid-searchhyperparameterstrain-test-split

解决方案

这取决于您为 GridSearchCV 指定的参数限制。

没有任何参数的决策树的参数默认值不在您手动指定的范围内。选择一组更好的参数并再次尝试 GridSearchCV。

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