X_train, X_test, y_train, y_test = train_test_split(df[data.feature_names], df['target'], test_size=0.2, random_state=42)与KNN中邻居数的关系
时间: 2023-10-30 11:49:26 浏览: 104
`train_test_split`函数是用于将数据集划分为训练集和测试集的函数,其中`test_size`参数指定测试集所占比例,`random_state`参数用于设置随机种子以确保每次运行都得到相同的结果。
而KNN算法中的邻居数是一个超参数,用于控制模型的复杂度。通常来说,邻居数越多,模型越简单,预测结果也越保守;邻居数越少,模型越复杂,预测结果也越激进。因此,邻居数需要通过交叉验证等方法进行调参。
在使用`train_test_split`函数划分数据集后,我们可以使用KNN算法对训练集进行拟合,并使用测试集进行评估。通过不断尝试不同的邻居数,我们可以找到最佳的超参数组合,以达到最好的预测效果。
相关问题
from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score from lime.lime_tabular import LimeTabularExplainer import numpy as np import pandas as pd # 准备数据 data = load_breast_cancer() # df=pd.DataFrame(data.data,columns=data.feature_names) # df['target']=data.target # print(df.head()) X = data.data y = data.target # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # 训练模型 rfc = RandomForestClassifier(n_estimators=100, random_state=42) rfc.fit(X_train, y_train) # 预测结果 y_pred = rfc.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy:{accuracy:.3f}") # 解释模型结果 def explain_sample(x, model, feature_names): explainer = LimeTabularExplainer(X_train, feature_names=feature_names, class_names=data.target_names) exp = explainer.explain_instance(x, model.predict_proba, num_features=len(feature_names)) return exp # 随机选择一个测试样本并解释 idx = np.random.randint(len(X_test)) x=X_test[idx] exp=explain_sample(x,rfc,data.feature_names) fig=exp.as_pyplot_figure() print(f"Sample index:{idx}") fig.show()优化一下这段代码,让可视化图片不要一闪而过
可以使用`input()`函数让程序等待用户输入后再继续执行,这样就可以让可视化图片在用户输入后再显示。代码如下:
```python
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from lime.lime_tabular import LimeTabularExplainer
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# 准备数据
data = load_breast_cancer()
X = data.data
y = data.target
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# 训练模型
rfc = RandomForestClassifier(n_estimators=100, random_state=42)
rfc.fit(X_train, y_train)
# 预测结果
y_pred = rfc.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy:{accuracy:.3f}")
# 解释模型结果
def explain_sample(x, model, feature_names):
explainer = LimeTabularExplainer(X_train, feature_names=feature_names, class_names=data.target_names)
exp = explainer.explain_instance(x, model.predict_proba, num_features=len(feature_names))
return exp
# 随机选择一个测试样本并解释
idx = np.random.randint(len(X_test))
x=X_test[idx]
exp=explain_sample(x,rfc,data.feature_names)
fig=exp.as_pyplot_figure()
print(f"Sample index:{idx}")
fig.show()
input("Press Enter to continue...")
```
这样,当程序运行到`fig.show()`时,会显示可视化图片,并等待用户输入后才会继续执行下去。用户可以在图片显示的状态下仔细观察,直到输入回车键后程序才会结束。
import pandas as pd df = pd.read_csv(filename, header=None, names=['category', 'text']) from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score # 读取新闻文本数据 from csv import readerimport numpy as np filename = input("请输入文件名:") with open(filename,'rt',encoding = 'UTF-8') as file: readers = reader(raw_data,delimiter=',') A = list(file) data = np.array(A)print(data) df = pd.read_csv('20news-18828') # 分割数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(df['text'], df['category'], random_state=42) # 对文本进行特征提取 tfidf = TfidfVectorizer(stop_words='english') X_train = X_train.astype(str) X_test = X_test.astype(str) # 使用多项式朴素贝叶斯模型进行训练和预测 clf = MultinomialNB() clf.fit(X_train_tfidf, y_train) y_pred = clf.predict(X_test_tfidf.toarray()) # 输出模型的准确率 print('Accuracy:', accuracy_score(y_test, y_pred))是否有问题
有几个问题需要修改:
1. 第一行代码中的`filename`是未定义的,需要将其定义或者替换成具体的文件名。
2. 第二行代码中的`header=None`是不必要的,因为后面显式地指定了列名。
3. 第三行代码中的`raw_data`是未定义的,需要将其替换成文件对象`file`。
4. 第四行代码中的`list(file)`可以直接替换成`file.readlines()`。
5. 第五行代码中的文件名应该是具体的文件名,而不是数据集的名称。
6. 第七行代码中的`train_test_split`函数未导入,需要将其导入。
7. 第十二行代码中的`X_train_tfidf`和`X_test_tfidf`未定义,需要先进行特征提取。
综上所述,可以修改为如下代码:
```python
import pandas as pd
import numpy as np
from csv import reader
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
filename = input("请输入文件名:")
with open(filename, 'rt', encoding='UTF-8') as file:
A = file.readlines()
data = np.array(A)
df = pd.DataFrame(data, columns=['text'])
# 分割数据集为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(df['text'], df['category'], random_state=42)
# 对文本进行特征提取
tfidf = TfidfVectorizer(stop_words='english')
X_train_tfidf = tfidf.fit_transform(X_train.astype(str))
X_test_tfidf = tfidf.transform(X_test.astype(str))
# 使用多项式朴素贝叶斯模型进行训练和预测
clf = MultinomialNB()
clf.fit(X_train_tfidf, y_train)
y_pred = clf.predict(X_test_tfidf.toarray())
# 输出模型的准确率
print('Accuracy:', accuracy_score(y_test, y_pred))
```
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