为什么混淆矩阵中的总数与数据输入不一样？

Question

为什么总混淆矩阵没有与数据集相同数量的 os 个样本？数据集包含 7514 但总混淆矩阵不超过 2000.

代码如下：

import re
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer

corpus = []
for i in range(len(dataset)):
  text = re.sub('[^a-zA-Z]', ' ', dataset['Text'][i])
  text = text.lower()
  text = text.split()
  ps = PorterStemmer()
  text = [ps.stem(word) for word in text if not word in set(stopwords.words('english'))]
  text = ' '.join(text)
  corpus.append(text)

import sklearn
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features = 10000)
X = cv.fit_transform(corpus).toarray()
y = dataset.iloc[:, 1].values

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)

from sklearn import linear_model
classifier = linear_model.LogisticRegression(C=10)
classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print ("Confusion Matrix:\n",cm)

from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
score1 = accuracy_score(y_test,y_pred)
score2 = precision_score(y_test,y_pred)
score3= recall_score(y_test,y_pred)
print("\n")
print("Accuracy is ",round(score1*100,2),"%")
print("Precision is ",round(score2,2))
print("Recall is ",round(score3,2))

Answer 1

使用 train_test_split 拆分数据后，测试部分剩下 2255 个样本，几乎等于 7514 X 0.3，然后使用这部分确定混淆矩阵（测试-部分）。现在一切都应该有意义了。

为什么混淆矩阵中的总数与数据输入不一样？

Why the the total number in confusion matrix not same as the data input?

python

machine-learning

scikit-learn

nlp

nltk