Python 中用于预测的逻辑回归分类器

Question

我正在尝试制作一个脚本，该脚本采用 json 文件（pizza-train.json）（来自 this Kaggle competition。我想从中提取 request_text 字段列表中的每个字典，并构造一个表示字符串的词袋（string to count-list）。

下一步是训练逻辑回归分类器来预测变量“requester_received_pizza”。我想训练 90% 的数据并预测 10% 的数据。问题是我不知道如何预测这 10%。任何建议都会很有帮助！

import json
from sklearn.cross_validation import train_test_split
from sklearn.feature_extraction.text import CountVectorizer


f_json = json.load(open('pizza-train.json'))
request_text = []
y = []

for item in f_json[:100]:
    request_text.append(item['request_text'])
    y.append(item['requester_received_pizza'])

vectorizer = CountVectorizer(min_df=1, lowercase=True, stop_words='english')

train_data_features = vectorizer.fit_transform(request_text)
train_data_features = train_data_features.toarray()


print 'Shape = '
print train_data_features.shape
vocab = vectorizer.get_feature_names()
print '\n'
print 'Vocab = '
print vocab


x_train, x_test, y_train, y_test = train_test_split(train_data_features, y, test_size=0.10)

Answer 1

你可以这样做：

alg = sklearn.linear_model.LogisticRegression()
alg.fit(x_train, y_train)
test_score = alg.score(x_test, y_test)

您应该阅读 sklearn 文档 logistic regression and cross validation, which are very good and provide more sophisticated methods for validating your models. This Kaggle 泰坦尼克号竞赛教程也可能有用。

Python 中用于预测的逻辑回归分类器

logistic regression classifier for prediction in Python

python

machine-learning

scikit-learn

kaggle