import jieba from nltk.classify import NaiveBayesClassifier # 数据准备 libai_features = ... # 与前文相似的加载过程
from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn import tree from sklearn.ensemble import RandomForestClassifier from sklearn.naive_bayes import GaussianNB from sklearn.neural_network import MLP...
naive_bayes import BernoulliNB, MultinomialNB from sklearn.neighbors import KNeighborsClassifier from sklearn.neighbors import NearestCentroid from sklearn.ensemble import RandomForestClassifier from sklearn.utils.extmath import density from sklearn import metrics from sklearn.cross_validation import train_...
在运行集成学习的多数投票分类代码时,出现错误 fromsklearnimportdatasetsfromsklearn.model_selectionimportcross_val_scorefromsklearn.linear_modelimportLogisticRegressionfromsklearn.naive_bayesimportGaussianNBfromsklearn.ensembleimportRandomForestClassifierfromsklearn.ensembleimportVotingClassifier iris = datasets.load_ir...
利用logistic回归对mnist数据集中的0和1进行分类_Logistic.zip 2024-12-26 11:15:29 积分:1 包含十七类数据集,以_!_分割的个字段。_-.zip 2024-12-26 08:22:51 积分:1 使用随机森林建立分类器对新闻数据集进行分类_RF_Classifier.zip 2024-12-26 08:19:55 积分:1...
MultinomialNB准确率为: 0.8960196779964222 SGDClassifier准确率为: 0.9724955277280859 LogisticRegression准确率为: 0.9304561717352415 SVC准确率为: 0.13372093023255813 LinearSVC准确率为: 0.9749552772808586 LinearSVR准确率为: 0.00022361359570661896 MLPClassifier准确率为: 0.9758497316636852 KNeighborsClassifier准确率为: ...
from sklearn.naive_bayes import GaussianNB from sklearn.ensemble import RandomForestClassifier 下面的可视化工具一次只能接受两个特征作为输入,所以我们创建了数组['proline', 'color_intensity']。因为这两个特征在上述利用ELI5分析时,具有最高的特征重要性。
from sklearn.naive_bayes import GaussianNB from sklearn.ensemble import GradientBoostingClassifier from xgboost import XGBClassifier,XGBRegressor from catboost import CatBoostClassifier,CatBoostRegressor from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor ...
classifier = naive_bayes.MultinomialNB 我们在特征矩阵上训练这个分类器,然后在经过特征提取后的测试集上测试它。因此我们需要一个scikit-learn流水线:这个流水线包含一系列变换和最后接一个estimator。将Tf-Idf向量器和朴素贝叶斯分类器放入流水线,就能轻松完成对测试数据的变换和预测。