Question

我正在尝试使用20个新闻组数据集运行随机森林算法，但我不知道如何解决问题。我之前使用SVM和NB使用相同的数据集，效果很好。

 from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer


dataset_train=fetch_20newsgroups(subset='train',shuffle=True)
dataset_test=fetch_20newsgroups(subset='test',shuffle=True)
vectorizer=CountVectorizer()

x_train_counts=vectorizer.fit_transform(dataset_train.data)

from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer=TfidfVectorizer(stop_words='english',lowercase=True,ngram_range=(1,5))
x_train_tfidf=vectorizer.fit_transform(dataset_train.data)

from sklearn.ensemble import RandomForestClassifier
model=RandomForestClassifier(n_estimators=10)
model=model.fit(dataset_train.data,dataset_train.target)

那就是错误：

    Traceback (most recent call last):
  File "C:/Users/new_randomforest.py", line 18, in <module>
    model=model.fit(dataset_train.data,dataset_train.target)
  File "C:\Users\forest.py", line 247, in fit
    X = check_array(X, accept_sparse="csc", dtype=DTYPE)
  File "C:\Users\validation.py", line 433, in check_array
    array = np.array(array, dtype=dtype, order=order, copy=copy)
ValueError: could not convert string to float: "From: lerxst@wam.umd.edu (where's my thing)\nSubject: WHAT car is this!?\nNntp-Posting-Host: rac3.wam.umd.edu\nOrganization: University of Maryland, College Park\nLines: 15\n\n I was wondering if anyone out there could enlighten me on this car I saw\nthe other day. It was a 2-door sports car, looked to be from the late 60s/\nearly 70s. It was called a Bricklin. The doors were really small. In addition,\nthe front bumper was separate from the rest of the body. This is \nall I know. If anyone can tellme a model name, engine specs, years\nof production, where this car is made, history, or whatever info you\nhave on this funky looking car, please e-mail.\n\nThanks,\n- IL\n   ---- brought to you by your neighborhood Lerxst ----\n\n\n\n\n"

Answer 1

您需要在拟合模型时使用您创建的火车矢量（此处为x_train_tfidf）

model.fit(x_train_tfidf,dataset_train.target)

dataset_train.data是一个字符串列表。这就是错误的原因。

PS :: 基本上错误说的是，您试图在模型中使用string，这是不允许的。引自documentation

fit(X, y, sample_weight=None)

X：形状的数组或稀疏矩阵= [n_samples，n_features]

培训输入样本。在内部，其dtype将转换为 dtype = np.float32 。如果提供稀疏矩阵，则将其转换为稀疏csc_matrix。

有20个新闻组的随机森林问题

1 个答案: