所有分类算法的列表

Question

我有分类问题,我想测试所有可用的算法来测试它们在解决问题时的表现.如果您知道除下面列出的分类算法以外的任何分类算法,请在此处列出.

GradientBoostingClassifier()
DecisionTreeClassifier()
RandomForestClassifier()
LinearDiscriminantAnalysis()
LogisticRegression()
KNeighborsClassifier()
GaussianNB()
ExtraTreesClassifier()
BaggingClassifier()

非常感谢您的帮助.

Answer 1

The answers did not provided the full list of classifiers so i have listed them below

from sklearn.tree import ExtraTreeClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm.classes import OneClassSVM
from sklearn.neural_network.multilayer_perceptron import MLPClassifier
from sklearn.neighbors.classification import RadiusNeighborsClassifier
from sklearn.neighbors.classification import KNeighborsClassifier
from sklearn.multioutput import ClassifierChain
from sklearn.multioutput import MultiOutputClassifier
from sklearn.multiclass import OutputCodeClassifier
from sklearn.multiclass import OneVsOneClassifier
from sklearn.multiclass import OneVsRestClassifier
from sklearn.linear_model.stochastic_gradient import SGDClassifier
from sklearn.linear_model.ridge import RidgeClassifierCV
from sklearn.linear_model.ridge import RidgeClassifier
from sklearn.linear_model.passive_aggressive import PassiveAggressiveClassifier    
from sklearn.gaussian_process.gpc import GaussianProcessClassifier
from sklearn.ensemble.voting_classifier import VotingClassifier
from sklearn.ensemble.weight_boosting import AdaBoostClassifier
from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier
from sklearn.ensemble.bagging import BaggingClassifier
from sklearn.ensemble.forest import ExtraTreesClassifier
from sklearn.ensemble.forest import RandomForestClassifier
from sklearn.naive_bayes import BernoulliNB
from sklearn.calibration import CalibratedClassifierCV
from sklearn.naive_bayes import GaussianNB
from sklearn.semi_supervised import LabelPropagation
from sklearn.semi_supervised import LabelSpreading
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegressionCV
from sklearn.naive_bayes import MultinomialNB  
from sklearn.neighbors import NearestCentroid
from sklearn.svm import NuSVC
from sklearn.linear_model import Perceptron
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.svm import SVC
from sklearn.mixture import DPGMM
from sklearn.mixture import GMM 
from sklearn.mixture import GaussianMixture
from sklearn.mixture import VBGMM

Answer 2

您可能想看看以下问题:

如何列出支持predict_proba()的所有scikit-learn分类器

接受的答案显示了获取scikit中支持predict_probas方法的所有估算器的方法.只需迭代并打印所有名称而不检查条件,即可获得所有估算器.(分类器,回归器,集群等)

仅对于分类器,请按如下所示进行修改,以检查实现ClassifierMixin的所有类

from sklearn.base import ClassifierMixin
from sklearn.utils.testing import all_estimators
classifiers=[est for est in all_estimators() if issubclass(est[1], ClassifierMixin)]
print(classifiers)

注意事项:

• CV后缀为其名称的分类器实现了内置的交叉验证(如LogisticRegressionCV,RidgeClassifierCV等).
• 有些是整体,可能会在输入参数中使用其他分类器.
• 某些分类器(如_QDA,_LDA)是其他分类器的别名,可能会在下一版本的scikit-learn中删除.

在使用它们之前,您应该检查它们各自的参考文档

Answer 3

另一种选择是使用模块from sklearn.utils import all_estimators。这是导入所有分类器的示例：

from sklearn.utils import all_estimators

estimators = all_estimators(type_filter='classifier')

all_clfs = []
for name, ClassifierClass in estimators:
    print('Appending', name)
    try:
        clf = ClassifierClass()
        all_clfs.append(clf)
    except Exception as e:
        print('Unable to import', name)
        print(e)

这是一个可以工作的 colab 代码。