04 2023 档案
摘要:探索核函数的优势和缺陷 from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.svm import SVC import ma
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摘要:探索核函数在不同数据集上的表现 导入模块 import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap from sklearn import svm from skle
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摘要:线性 SVM 决策过程的可视化 导入模块 from sklearn.datasets import make_blobs from sklearn.svm import SVC import matplotlib.pyplot as plt import numpy as np 实例化数据集,可视化
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摘要:from sklearn.datasets import make_blobs import matplotlib.pyplot as plt x, y = make_blobs(n_samples=500, n_features=2, centers=4, random_state=1) colo
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摘要:评分卡案例 数据预处理 %matplotlib inline # 导入库 import pandas as pd import numpy as np from sklearn.linear_model import LogisticRegression as LR # 加载数据 data = pd
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摘要:from sklearn.datasets import load_breast_cancer from sklearn.feature_selection import SelectFromModel from sklearn.linear_model import LogisticRegress
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摘要:from sklearn.neighbors import KNeighborsClassifier as KNN from sklearn.decomposition import PCA from sklearn.ensemble import RandomForestClassifier as
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摘要:import pandas as pd import numpy as np import matplotlib.pyplot as plt # 准备数据 data = pd.read_csv("./digit recognizor.csv") x = data.iloc[:,1:] # 特征矩阵
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摘要:import pandas as pd data = pd.read_csv("./digit recognizor.csv") x = data.iloc[:,1:] y = data.iloc[:,0] x.shape (42000, 784) 方差过滤 VarianceThreshold fr
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摘要:二值化与分段 sklearn.preprocessing.Binarizer from sklearn.preprocessing import Binarizer import pandas as pd data = pd.read_csv("./data_full", index_col=0)
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摘要:处理缺失值 import pandas as pd import numpy as np df = pd.read_csv("./Narrativedata.csv", index_col=0) df.info() <class 'pandas.core.frame.DataFrame'> Int6
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摘要:数据归一化 import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler data = np.arange(36) data = data.reshape(6,6) data = pd.Da
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摘要:随机森林在乳腺癌数据上的调参 导入需要的库 from sklearn.datasets import load_breast_cancer from sklearn.model_selection import GridSearchCV, cross_val_score from sklearn.e
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摘要:import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestRegressor from sklearn.impute import S
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摘要:随机森林 单颗树与随机森林的的分对比 # 导入包 from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split from sklearn.tree import Decision
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摘要:泰坦尼克号生还预测 导入模块 import pandas as pd # 数据处理 import matplotlib.pyplot as plt # 画图 from sklearn.tree import DecisionTreeClassifier # 决策树模型 from sklearn.mo
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摘要:DecisionTreeClassifier from sklearn.datasets import load_wine # 红酒数据集 from sklearn.tree import DecisionTreeClassifier, export_graphviz # 决策树, 画树 from
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