解释MNB.fit(X[100:], y[100:])
时间: 2024-01-06 20:06:52 浏览: 64
MNB.fit(X[100:], y[100:])是一个机器学习中使用朴素贝叶斯算法训练分类器的方法。其中,X[100:]是训练数据的输入特征,y[100:]是训练数据的输出类型,fit()函数用于将训练数据拟合到分类器中进行训练。具体来说,该方法可以训练一个朴素贝叶斯分类器来预测给定输入特征的输出类型。
相关问题
import pickle import time from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import classification_report import numpy as np with open('体育.pkl', 'rb') as f: sports_news = pickle.load(f) # 加载行政新闻数据集 with open('从政.pkl', 'rb') as f: politics_news = pickle.load(f) # 将体育新闻和行政新闻合并成一个数据集 data = np.concatenate([politics_news, sports_news]) def load_variavle(filename): f = open(filename, 'rb') r = pickle.load(f) f.close() return r news = np.concatenate([politics_news, sports_news]) print(news.data[0]) print(news.target_names) print(news.target[0:20]) X_train,X_test,y_train,y_test=train_test_split(news.data,news.target,test_size=0.25,random_state=520) vec =TfidfVectorizer(stop_words='english') X_train=vec.fit_transform(X_train) X_test=vec.transform(X_test) time_start = time.perf_counter() mnb=MultinomialNB() mnb.fit(X_train,y_train) y_predict=mnb.predict(X_test) print('训练时间',time_start) print('准确率:',mnb.score(X_test,y_test)) print(classification_report(y_test,y_predict,target_names=news.target_names))
这段代码主要是导入一些Python第三方库,包括pickle、time、sklearn等。同时使用了TfidfVectorizer、train_test_split、MultinomialNB和classification_report等模块,用于数据的处理、模型的训练和评估。最后从名为“体育.pkl”的文件中读取了数据,并将其存储在变量sports_news中。
编程;基于Skit-earn自带的白酒数据集完成机器学习的数据准备工作,并将代码填入答案但中。要求如下 (1)加载Scikit-learn自带的白酒数据集; (2)获取数据集的特征 (X)和标签(Y),并打印X和Y的大小; (3)将数据集划分为训练集和测试集,并打印训练集和测试集的大小。 (4)选择数据集的两个特征,绘制数据集散点图(scatter) 编程:基于上述加载拆分后的白酒数据集使用对数几率回归(Logistic Regression)进行分类,评估结果,并 打印混淆矩阵(confusion matrix)和分类报告(classificationreport)。 (1)初始化对数几率回归模型,使用训练集进行拟合; (2)使用测试集测试分类精度,预测测试数据,并打印结果; (3)打印分类报告。 (4)打印混淆矩阵。 提示:对数几率回归所在包sklearn.linear model,混淆矩阵和分类报告所在包:sklearn.metrics 编程:基于自酒数据集,使用朴素贝叶斯方法编程实现如下功能: (1)分别使用高斯朴素贝叶斯和多项式朴素贝叶斯训练数据; (2)打印两个朴素贝叶斯的测试精度; (3)定义分层划分(n splits=50,test size=0.2),利用学习曲线函数(leaming curve)计算两个方法的 测试集大小、训练精度和测试精度,并打印。 (4)分别绘制两种朴素贝叶斯方法的学习曲线。
(1) 加载Scikit-learn自带的白酒数据集:
```python
from sklearn.datasets import load_wine
wine = load_wine()
```
(2) 获取数据集的特征 X 和标签 Y ,并打印 X 和 Y 的大小:
```python
X = wine.data
Y = wine.target
print('X size:', X.shape)
print('Y size:', Y.shape)
```
(3) 将数据集划分为训练集和测试集,并打印训练集和测试集的大小:
```python
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=42)
print('X_train size:', X_train.shape)
print('X_test size:', X_test.shape)
print('Y_train size:', Y_train.shape)
print('Y_test size:', Y_test.shape)
```
(4) 选择数据集的两个特征,绘制数据集散点图(scatter):
```python
import matplotlib.pyplot as plt
plt.scatter(X[:, 0], X[:, 1], c=Y)
plt.xlabel('Feature 0')
plt.ylabel('Feature 1')
plt.show()
```
基于上述加载拆分后的白酒数据集使用对数几率回归(Logistic Regression)进行分类,评估结果,并打印混淆矩阵(confusion matrix)和分类报告(classification report):
(1) 初始化对数几率回归模型,使用训练集进行拟合:
```python
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, Y_train)
```
(2) 使用测试集测试分类精度,预测测试数据,并打印结果:
```python
from sklearn.metrics import accuracy_score
Y_pred = lr.predict(X_test)
print('Accuracy:', accuracy_score(Y_test, Y_pred))
```
(3) 打印分类报告:
```python
from sklearn.metrics import classification_report
print(classification_report(Y_test, Y_pred))
```
(4) 打印混淆矩阵:
```python
from sklearn.metrics import confusion_matrix
print(confusion_matrix(Y_test, Y_pred))
```
基于自酒数据集,使用朴素贝叶斯方法编程实现如下功能:
(1) 分别使用高斯朴素贝叶斯和多项式朴素贝叶斯训练数据:
```python
from sklearn.naive_bayes import GaussianNB, MultinomialNB
gnb = GaussianNB()
mnb = MultinomialNB()
gnb.fit(X_train, Y_train)
mnb.fit(X_train, Y_train)
```
(2) 打印两个朴素贝叶斯的测试精度:
```python
print('GaussianNB accuracy:', gnb.score(X_test, Y_test))
print('MultinomialNB accuracy:', mnb.score(X_test, Y_test))
```
(3) 定义分层划分(n_splits=50, test_size=0.2),利用学习曲线函数(learning curve)计算两个方法的测试集大小、训练精度和测试精度,并打印:
```python
from sklearn.model_selection import learning_curve
train_sizes, train_scores_gnb, test_scores_gnb = learning_curve(GaussianNB(), X, Y, cv=50, train_sizes=np.linspace(0.1, 1.0, 5))
train_sizes, train_scores_mnb, test_scores_mnb = learning_curve(MultinomialNB(), X, Y, cv=50, train_sizes=np.linspace(0.1, 1.0, 5))
print('GaussianNB train_sizes:', train_sizes)
print('GaussianNB train_scores:', np.mean(train_scores_gnb, axis=1))
print('GaussianNB test_scores:', np.mean(test_scores_gnb, axis=1))
print('MultinomialNB train_sizes:', train_sizes)
print('MultinomialNB train_scores:', np.mean(train_scores_mnb, axis=1))
print('MultinomialNB test_scores:', np.mean(test_scores_mnb, axis=1))
```
(4) 分别绘制两种朴素贝叶斯方法的学习曲线:
```python
plt.plot(train_sizes, np.mean(train_scores_gnb, axis=1), 'o-', color='r', label='GaussianNB training score')
plt.plot(train_sizes, np.mean(test_scores_gnb, axis=1), 'o-', color='g', label='GaussianNB test score')
plt.plot(train_sizes, np.mean(train_scores_mnb, axis=1), 'o-', color='b', label='MultinomialNB training score')
plt.plot(train_sizes, np.mean(test_scores_mnb, axis=1), 'o-', color='y', label='MultinomialNB test score')
plt.xlabel('Training examples')
plt.ylabel('Score')
plt.legend(loc='best')
plt.show()
```
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