lr = LogisticRegression() lr.fit(x_train,y_train) print("Logistic Regression Accuracy: ",lr.score(x_test,y_test))
时间: 2024-05-31 10:11:38 浏览: 16
这段代码使用scikit-learn库中的Logistic Regression模型进行训练和测试,并输出模型在测试集上的准确率。具体来说,lr.fit(x_train,y_train)是用训练集x_train和对应的标签y_train来训练模型,lr.score(x_test,y_test)计算模型在测试集x_test和对应的标签y_test上的准确率,并将结果打印出来。需要注意的是,这里的准确率指的是模型预测正确的样本占总样本数的比例。
相关问题
报错ValueError: np.nan is an invalid document, expected byte or unicode string. 怎么修改import pandas as pd from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # 读取电影评论数据集 data = pd.read_csv(r'D:\shujukexue\review_data.csv', encoding='gbk') x = v.fit_transform(df['eview'].apply(lambda x: np.str_(x))) # 分割数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42) # 创建CountVectorizer对象进行词频统计和向量化 count_vectorizer = CountVectorizer() X_train_count = count_vectorizer.fit_transform(X_train) X_test_count = count_vectorizer.transform(X_test) # 创建TfidfVectorizer对象进行TF-IDF计算和向量化 tfidf_vectorizer = TfidfVectorizer() X_train_tfidf = tfidf_vectorizer.fit_transform(X_train) X_test_tfidf = tfidf_vectorizer.transform(X_test) # 创建逻辑回归分类器并在CountVectorizer上进行训练和预测 classifier_count = LogisticRegression() classifier_count.fit(X_train_count, y_train) y_pred_count = classifier_count.predict(X_test_count) accuracy_count = accuracy_score(y_test, y_pred_count) print("Accuracy using CountVectorizer:", accuracy_count) # 创建逻辑回归分类器并在TfidfVectorizer上进行训练和预测 classifier_tfidf = LogisticRegression() classifier_tfidf.fit(X_train_tfidf, y_train) y_pred_tfidf = classifier_tfidf.predict(X_test_tfidf) accuracy_tfidf = accuracy_score(y_test, y_pred_tfidf) print("Accuracy using TfidfVectorizer:", accuracy_tfidf)
在代码中,变量名应该是 'data' 而不是 'df',所以在使用 CountVectorizer 进行向量化的时候,应该使用 'data' 而不是 'df',修改代码如下所示:
```
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# 读取电影评论数据集
data = pd.read_csv(r'D:\shujukexue\review_data.csv', encoding='gbk')
# 分割数据集为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42)
# 创建CountVectorizer对象进行词频统计和向量化
count_vectorizer = CountVectorizer()
X_train_count = count_vectorizer.fit_transform(X_train)
X_test_count = count_vectorizer.transform(X_test)
# 创建TfidfVectorizer对象进行TF-IDF计算和向量化
tfidf_vectorizer = TfidfVectorizer()
X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)
X_test_tfidf = tfidf_vectorizer.transform(X_test)
# 创建逻辑回归分类器并在CountVectorizer上进行训练和预测
classifier_count = LogisticRegression()
classifier_count.fit(X_train_count, y_train)
y_pred_count = classifier_count.predict(X_test_count)
accuracy_count = accuracy_score(y_test, y_pred_count)
print("Accuracy using CountVectorizer:", accuracy_count)
# 创建逻辑回归分类器并在TfidfVectorizer上进行训练和预测
classifier_tfidf = LogisticRegression()
classifier_tfidf.fit(X_train_tfidf, y_train)
y_pred_tfidf = classifier_tfidf.predict(X_test_tfidf)
accuracy_tfidf = accuracy_score(y_test, y_pred_tfidf)
print("Accuracy using TfidfVectorizer:", accuracy_tfidf)
```
1. 采用Python编程语言自己实现StandardScaler和MinMaxScaler这两种数据标准化方法,并用于乳腺癌数据的分类。 要求模型预测的准确率结果必须与以下sklearn的一样: from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split cancer = load_breast_cancer() X = cancer.data y = cancer.target X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.3) from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import StandardScaler scaler = StandardScaler() #scaler = MinMaxScaler() scaler.fit(X_train) X_train_scaled = scaler.transform(X_train) X_test_scaled = scaler.transform(X_test) from sklearn.linear_model import LogisticRegression logreg=LogisticRegression() logreg.fit(X_train_scaled, y_train) y_pred = logreg.predict(X_test_scaled) from sklearn.metrics import accuracy_score accuracy_score(y_test, y_pred)
好的,这是一个很好的问题!数据标准化是数据预处理中非常重要的一步,可以帮助我们更好地训练模型。下面是我为你提供的Python代码,可以自己实现StandardScaler和MinMaxScaler这两种数据标准化方法,并用于乳腺癌数据的分类。
```
import numpy as np
class StandardScaler:
def __init__(self):
self.mean_ = None
self.std_ = None
def fit(self, X):
self.mean_ = np.mean(X, axis=0)
self.std_ = np.std(X, axis=0)
def transform(self, X):
return (X - self.mean_) / self.std_
class MinMaxScaler:
def __init__(self):
self.min_ = None
self.max_ = None
def fit(self, X):
self.min_ = np.min(X, axis=0)
self.max_ = np.max(X, axis=0)
def transform(self, X):
return (X - self.min_) / (self.max_ - self.min_)
# 加载乳腺癌数据
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
cancer = load_breast_cancer()
X = cancer.data
y = cancer.target
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.3)
# 使用自己实现的StandardScaler进行数据标准化
scaler = StandardScaler()
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)
# 使用逻辑回归进行分类
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_train_scaled, y_train)
y_pred = logreg.predict(X_test_scaled)
# 计算准确率
from sklearn.metrics import accuracy_score
print("Accuracy score:", accuracy_score(y_test, y_pred))
```
这段代码中,我们分别实现了StandardScaler和MinMaxScaler这两种数据标准化方法,并且使用逻辑回归进行分类。最后,我们计算了模型预测的准确率结果,应该与sklearn的一样。
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