摘要：import numpy as np import matplotlib.pyplot as plt from sklearn.svm import LinearSVC from sklearn.datasets import load_digits from sklearn.model_selection import validation_curve #模型选择验证曲线validatio... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn.svm import LinearSVC from sklearn.datasets import load_digits from sklearn.model_selection import learning_curve #模型选择学习曲线learning_cu... 阅读全文

摘要：from sklearn.metrics import mean_absolute_error,mean_squared_error #模型选择回归问题性能度量mean_absolute_error模型 def test_mean_absolute_error(): y_true=[1,1,1,1,1,2,2,2,0,0] y_pred=[0,0,0,1,1,1,0,0,0,0... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn.svm import SVC from sklearn.datasets import load_iris from sklearn.preprocessing import label_binarize from sklearn.multiclass impo... 阅读全文

摘要：import numpy as np from sklearn.model_selection import train_test_split,KFold,StratifiedKFold,LeaveOneOut,cross_val_score #模型选择数据集切分train_test_split模型 def test_train_test_split(): X=[[1,2,3,4],... 阅读全文

摘要：from sklearn.metrics import zero_one_loss,log_loss def test_zero_one_loss(): y_true=[1,1,1,1,1,0,0,0,0,0] y_pred=[0,0,0,1,1,1,1,1,0,0] print("zero_one_loss:",zero_one_loss(y_true,y_pred,... 阅读全文

摘要：from sklearn.decomposition import DictionaryLearning #数据预处理字典学习DictionaryLearning模型 def test_DictionaryLearning(): X=[[1,2,3,4,5], [6,7,8,9,10], [10,9,8,7,6,], [5,4,3,2,1]] ... 阅读全文

摘要：from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn import neighbors, datasets from sklearn.datasets import load_digits from sklearn.linear_model import LogisticRegre... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn.svm import LinearSVC from sklearn.linear_model import Lasso from sklearn.model_selection import train_test_split from sklearn.feature... 阅读全文

摘要：from sklearn.svm import LinearSVC from sklearn.datasets import load_iris from sklearn.feature_selection import RFE,RFECV from sklearn.model_selection import train_test_split #数据预处理包裹式特征选取RFE模型 def t... 阅读全文

摘要：from sklearn.feature_selection import SelectPercentile,f_classif #数据预处理过滤式特征选取SelectPercentile模型 def test_SelectKBest(): X=[[1,2,3,4,5], [5,4,3,2,1], [3,3,3,3,3,], ... 阅读全文

摘要：from sklearn.feature_selection import SelectKBest,f_classif #数据预处理过滤式特征选取SelectKBest模型 def test_SelectKBest(): X=[[1,2,3,4,5], [5,4,3,2,1], [3,3,3,3,3,], [1,1,1,1,1... 阅读全文

摘要：from sklearn.preprocessing import Normalizer #数据预处理正则化Normalizer模型 def test_Normalizer(): X=[[1,2,3,4,5], [5,4,3,2,1], [1,3,5,2,4,], [2,4,1,3,5]] print("before ... 阅读全文

摘要：from sklearn.preprocessing import MaxAbsScaler #数据预处理标准化MaxAbsScaler模型 def test_MaxAbsScaler(): X=[[1,5,1,2,10], [2,6,3,2,7], [3,7,5,6,4,], [4,8,7,8,1]] print("before trans... 阅读全文

摘要：from sklearn.preprocessing import StandardScaler #数据预处理标准化StandardScaler模型 def test_StandardScaler(): X=[[1,5,1,2,10], [2,6,3,2,7], [3,7,5,6,4,], [4,8,7,8,1]] print("before... 阅读全文

摘要：from sklearn.preprocessing import MinMaxScaler #数据预处理标准化MinMaxScaler模型 def test_MinMaxScaler(): X=[[1,5,1,2,10], [2,6,3,2,7], [3,7,5,6,4,], [4,8,7,8,1]] print("before trans... 阅读全文

摘要：from sklearn.preprocessing import OneHotEncoder #数据预处理二元化OneHotEncoder模型 def test_OneHotEncoder(): X=[[1,2,3,4,5], [5,4,3,2,1], [3,3,3,3,3,], [1,1,1,1,1]] print... 阅读全文

摘要：from sklearn.preprocessing import Binarizer #数据预处理二元化Binarizer模型 def test_Binarizer(): X=[[1,2,3,4,5], [5,4,3,2,1], [3,3,3,3,3,], [1,1,1,1,1]] print("before tra... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn import datasets,ensemble from sklearn.model_selection import train_test_split def load_data_regression(): ''' 加载用于回归问题的数据集 ... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn import datasets,ensemble from sklearn.model_selection import train_test_split def load_data_classification(): ''' 加载用于分类问题的数据... 阅读全文