--从身份证获取年龄 select to_char(to_date(sysdate,'yyyy'))-sm system.dual; --从身份证获取性别SELECT CASE (substr(cust_id,17,1)%2)WHEN 1 THEN '1'WHEN 0 THEN '2'END AS 's...
归一化后有两个好处:(1)归一化后加快了梯度下降求最优解的速度。(2)归一化有可能提高精度(归一
训练集,验证集,测试集比例当数据量比较小时,可以使用 7 :3 训练数据和测试数据,或者 6:2 : 2 训练数据,验
sparksql_可视化组分布_histogram # 如果数据是几百万行,第二种方法显然不可取。因此需要先聚合数据。hists = fraud_df.select('balance').rdd.flatMap(lambda row: row).histogram(20)To plot the histogram you can simply call the matplotlib like b
python_特征转化_apply_FunctionTransformer对特征进行转化# 라이브러리를 임포트합니다.import numpy as npfrom sklearn.preprocessing import FunctionTransformer# 创建矩阵features = np.array([[2, 3], [2, 3...
python_归一化最大最小值 MinMaxScaler4.1 Rescaling a feature¶Use scikit-learn's MinMaxScaler to rescale a feature array# 数据缩放 归一化 最大最小值import numpy as npfrom sklearn import preprocessing# create a...
如何观察数据分布_describe从上面的描述性统计可以看出两点:**正偏态离散程度**1)所有的特征都是正倾斜的,最大值是平均数的几
python_异常值_EllipticEnvelope法和四分位差法# 加载库import numpy as npfrom sklearn.covariance import EllipticEnvelopefrom sklearn.datasets import make_blobs# 创建爱模拟数据# sklearn 中 make_blobs模块使用# sklearn.d...
python_对异常值进行处理_丢弃_转化# 方法一: 丢弃# 加载库import pandas as pd# 创建数据集houses = pd.DataFrame()houses['Price'] = [534433, 392333, 293222, 4322032]houses['Bathrooms'] = [2, 3.5, 2, 116]houses['Squar...
LabelBinarizer进行单分类和多分类one-hot编码5.1 Encoding Nominal Categorical Feature¶feature# 加载库 使用LabelBinarizer 进行one-hot编码import numpy as npfrom sklearn.preprocessing import LabelBinarizer, MultiLabel...
pyhton_聚类进行分组_分箱_离散化# 使用聚类进行离散化, 分箱import pandas as pdfrom sklearn.datasets import make_blobsfrom sklearn.cluster import KMeansfeatures, _ = make_blobs(n_samples = 50, ...
python_通过KNN来填充缺失值# 加载库import numpy as npfrom fancyimpute import KNNfrom sklearn.preprocessing import StandardScalerfrom sklearn.datasets import make_blobs# 创建模拟特征矩阵features, _ = make_blobs(n_...
使用replace 映射,处理存在天然顺序的字符串数据5.2 Encoding Ordinal Categorical Features¶import pandas as pd# create featuresdf = pd.DataFrame({"Score": ["Low", "Low", "Medium", "Medium", "High"]})dfScore0 Low...
Copyright © 2005-2025 51CTO.COM 版权所有 京ICP证060544号