当前位置： 首页 > news >正文

手机网站自适应网上培训机构

news 2025/7/1 20:29:47
手机网站自适应,网上培训机构,wordpress权限 ssh,企业电子商务网站建设的必要性目录 DAY 18 推断聚类后簇的类型1.推断簇含义的2个思路：先选特征和后选特征2.通过可视化图形借助ai定义簇的含义3.科研逻辑闭环:通过精度判断特征工程价值作业：参考示例代码对心脏病数据集采取类似操作，并且评估特征工程后模型效果有无提升。…

目录

      • DAY 18 推断聚类后簇的类型
        • 1.推断簇含义的2个思路：先选特征和后选特征
        • 2.通过可视化图形借助ai定义簇的含义
        • 3.科研逻辑闭环:通过精度判断特征工程价值
        • 作业：参考示例代码对心脏病数据集采取类似操作，并且评估特征工程后模型效果有无提升。

DAY 18 推断聚类后簇的类型

聚类后的分析：推断簇的类型

import seaborn as sns
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
import numpy as np
import warnings
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from catboost import CatBoostClassifier
from sklearn.ensemble import RandomForestClassifier
import lightgbm as lgb
import xgboost as xgb
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
import time
from sklearn.model_selection import train_test_split
import pandas as pd
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = Falsedata = pd.read_csv(r'data.csv')list_discrete = data.select_dtypes(include=['object']).columns.tolist()home_ownership_mapping = {'Own Home': 1, 'Rent': 2,'Have Mortgage': 3, 'Home Mortgage': 4}
data['Home Ownership'] = data['Home Ownership'].map(home_ownership_mapping)years_in_job_mapping = {'< 1 year': 1, '1 year': 2, '2 years': 3, '3 years': 4, '4 years': 5,'5 years': 6, '6 years': 7, '7 years': 8, '8 years': 9, '9 years': 10, '10+ years': 11}
data['Years in current job'] = data['Years in current job'].map(years_in_job_mapping)data = pd.get_dummies(data, columns=['Purpose'])
data2 = pd.read_csv(r'data.csv')
list_new = []
for i in data.columns:if i not in data2.columns:list_new.append(i)
for i in list_new:data[i] = data[i].astype(int)term_mapping = {'Short Term': 0, 'Long Term': 1}
data['Term'] = data['Term'].map(term_mapping)
data.rename(columns={'Term': 'Long Term'}, inplace=True)list_continuous = data.select_dtypes(include=['int64', 'float64']).columns.tolist()for i in list_continuous:median_value = data[i].median()data[i] = data[i].fillna(median_value)X = data.drop(['Credit Default'], axis=1)
Y = data['Credit Default']
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score
import matplotlib.pyplot as pltk_range = range(2, 11)
inertia_values = []
silhouette_scores = []
ch_scores = []
db_scores = []for k in k_range:kmeans = KMeans(n_clusters=k, random_state=42)kmeans_labels = kmeans.fit_predict(X_scaled)inertia_values.append(kmeans.inertia_)silhouette = silhouette_score(X_scaled, kmeans_labels)silhouette_scores.append(silhouette)ch = calinski_harabasz_score(X_scaled, kmeans_labels)ch_scores.append(ch)db = davies_bouldin_score(X_scaled, kmeans_labels)db_scores.append(db)print(f'k = {k}, 惯性: {kmeans.inertia_:.2f}, 轮廓系数: {silhouette:.3f}, CH 指数: {ch:.2f}, DB 指数: {db:.3f}')selected_k = 3kmeans = KMeans(n_clusters=selected_k, random_state=42)
kmeans_labels = kmeans.fit_predict(X_scaled)
X['KMeans_Cluster'] = kmeans_labelspca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)plt.figure(figsize=(6, 5))
sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1],hue=kmeans_labels, palette='viridis')
plt.title(f'KMeans Clustering with k = {selected_k} (PCA Visualization)')
plt.xlabel('PCA Component 1')
plt.ylabel('PCA Component 2')
plt.show()print(f"KMeans Cluster labels (k = {selected_k}) added to X:")
print(X[['KMeans_Cluster']].value_counts())
k = 2, 惯性: 224921.38, 轮廓系数: 0.723, CH 指数: 252.64, DB 指数: 0.355
k = 3, 惯性: 210919.39, 轮廓系数: 0.320, CH 指数: 383.53, DB 指数: 2.446
k = 4, 惯性: 204637.65, 轮廓系数: 0.087, CH 指数: 340.21, DB 指数: 2.315
k = 5, 惯性: 198854.98, 轮廓系数: 0.106, CH 指数: 317.03, DB 指数: 2.232
k = 6, 惯性: 191274.31, 轮廓系数: 0.112, CH 指数: 323.04, DB 指数: 1.921
k = 7, 惯性: 183472.98, 轮廓系数: 0.121, CH 指数: 333.71, DB 指数: 1.750
k = 8, 惯性: 174533.93, 轮廓系数: 0.131, CH 指数: 355.46, DB 指数: 2.089
k = 9, 惯性: 167022.49, 轮廓系数: 0.133, CH 指数: 367.09, DB 指数: 1.862
k = 10, 惯性: 163353.82, 轮廓系数: 0.097, CH 指数: 352.27, DB 指数: 1.838

在这里插入图片描述

KMeans Cluster labels (k = 3) added to X:
KMeans_Cluster
0                 5953
1                 1451
2                   96
Name: count, dtype: int64

X.columns
Index(['Id', 'Home Ownership', 'Annual Income', 'Years in current job','Tax Liens', 'Number of Open Accounts', 'Years of Credit History','Maximum Open Credit', 'Number of Credit Problems','Months since last delinquent', 'Bankruptcies', 'Long Term','Current Loan Amount', 'Current Credit Balance', 'Monthly Debt','Credit Score', 'Purpose_business loan', 'Purpose_buy a car','Purpose_buy house', 'Purpose_debt consolidation','Purpose_educational expenses', 'Purpose_home improvements','Purpose_major purchase', 'Purpose_medical bills', 'Purpose_moving','Purpose_other', 'Purpose_renewable energy', 'Purpose_small business','Purpose_take a trip', 'Purpose_vacation', 'Purpose_wedding','KMeans_Cluster'],dtype='object')
1.推断簇含义的2个思路：先选特征和后选特征
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import shapx1 = X.drop('KMeans_Cluster', axis=1)
y1 = X['KMeans_Cluster']
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(x1, y1)
shap.initjs()
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(x1)
shap_values.shape
(7500, 31, 3)
2.通过可视化图形借助ai定义簇的含义
print('SHAP 特征重要性条形图')
shap.summary_plot(shap_values[:, :, 0], x1, plot_type='bar', show=False)
plt.title('SHAP Feature Importance (Bar Plot)')
plt.show()
SHAP 特征重要性条形图

在这里插入图片描述

selected_features = ['Purpose_debt consolidation', 'Purpose_other','Purpose_home improvements', 'Purpose_business loan']for feature in selected_features:unique_count = X[feature].nunique()print(f'{feature} 的唯一值数量: {unique_count}')if unique_count < 10:print(f'{feature} 可能是离散型变量')else:print(f'{feature} 可能是连续型变量')
Purpose_debt consolidation 的唯一值数量: 2
Purpose_debt consolidation 可能是离散型变量
Purpose_other 的唯一值数量: 2
Purpose_other 可能是离散型变量
Purpose_home improvements 的唯一值数量: 2
Purpose_home improvements 可能是离散型变量
Purpose_business loan 的唯一值数量: 2
Purpose_business loan 可能是离散型变量

import matplotlib.pyplot as pltfig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.flatten()for i, feature in enumerate(selected_features):axes[i].hist(X[feature], bins=20)axes[i].set_title(f'Histogram of {feature}')axes[i].set_xlabel(feature)axes[i].set_ylabel('Frequency')plt.tight_layout()
plt.show()

在这里插入图片描述

X[['KMeans_Cluster']].value_counts()
KMeans_Cluster
0                 5953
1                 1451
2                   96
Name: count, dtype: int64

X_cluster0 = X[X['KMeans_Cluster'] == 0]
X_cluster1 = X[X['KMeans_Cluster'] == 1]
X_cluster2 = X[X['KMeans_Cluster'] == 2]
import matplotlib.pyplot as pltfig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.flatten()for i, feature in enumerate(selected_features):axes[i].hist(X_cluster0[feature], bins=20)axes[i].set_title(f'Histogram of {feature}')axes[i].set_xlabel(feature)axes[i].set_ylabel('Frequency')plt.tight_layout()
plt.show()

在这里插入图片描述

import matplotlib.pyplot as pltfig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.flatten()for i, feature in enumerate(selected_features):axes[i].hist(X_cluster1[feature], bins=20)axes[i].set_title(f'Histogram of {feature}')axes[i].set_xlabel(feature)axes[i].set_ylabel('Frequency')plt.tight_layout()
plt.show()

在这里插入图片描述

import matplotlib.pyplot as pltfig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.flatten()for i, feature in enumerate(selected_features):axes[i].hist(X_cluster2[feature], bins=20)axes[i].set_title(f'Histogram of {feature}')axes[i].set_xlabel(feature)axes[i].set_ylabel('Frequency')plt.tight_layout()
plt.show()

在这里插入图片描述

3.科研逻辑闭环:通过精度判断特征工程价值
作业：参考示例代码对心脏病数据集采取类似操作，并且评估特征工程后模型效果有无提升。
import matplotlib.pyplot as plt
import time
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
import xgboost as xgb
import lightgbm as lgb
from sklearn.ensemble import RandomForestClassifier
from catboost import CatBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.metrics import classification_report, confusion_matrix
import warnings
import numpy as np
from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import seaborn as sns
from sklearn.model_selection import train_test_split
import pandas as pd
warnings.filterwarnings('ignore')plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = Falsedata = pd.read_csv(r'heart.csv')X = data.drop(['target'], axis=1)
Y = data['target']
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score
import matplotlib.pyplot as pltk_range = range(2, 11)
inertia_values = []
silhouette_scores = []
ch_scores = []
db_scores = []for k in k_range:kmeans = KMeans(n_clusters=k, random_state=42)kmeans_labels = kmeans.fit_predict(X_scaled)inertia_values.append(kmeans.inertia_)silhouette = silhouette_score(X_scaled, kmeans_labels)silhouette_scores.append(silhouette)ch = calinski_harabasz_score(X_scaled, kmeans_labels)ch_scores.append(ch)db = davies_bouldin_score(X_scaled, kmeans_labels)db_scores.append(db)print(f'k = {k}, 惯性: {kmeans.inertia_:.2f}, 轮廓系数: {silhouette:.3f}, CH 指数: {ch:.2f}, DB 指数: {db:.3f}')selected_k = 3kmeans = KMeans(n_clusters=selected_k, random_state=42)
kmeans_labels = kmeans.fit_predict(X_scaled)
X['KMeans_Cluster'] = kmeans_labelspca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)plt.figure(figsize=(6, 5))
sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1],hue=kmeans_labels, palette='viridis')
plt.title(f'KMeans Clustering with k = {selected_k} (PCA Visualization)')
plt.xlabel('PCA Component 1')
plt.ylabel('PCA Component 2')
plt.show()print(f"KMeans Cluster labels (k = {selected_k}) added to X:")
print(X[['KMeans_Cluster']].value_counts())
k = 2, 惯性: 3331.64, 轮廓系数: 0.166, CH 指数: 54.87, DB 指数: 2.209
k = 3, 惯性: 3087.69, 轮廓系数: 0.112, CH 指数: 41.36, DB 指数: 2.544
k = 4, 惯性: 2892.52, 轮廓系数: 0.118, CH 指数: 36.06, DB 指数: 2.175
k = 5, 惯性: 2814.65, 轮廓系数: 0.094, CH 指数: 29.76, DB 指数: 2.386
k = 6, 惯性: 2673.22, 轮廓系数: 0.095, CH 指数: 28.13, DB 指数: 2.377
k = 7, 惯性: 2596.68, 轮廓系数: 0.088, CH 指数: 25.50, DB 指数: 2.290
k = 8, 惯性: 2464.39, 轮廓系数: 0.115, CH 指数: 25.22, DB 指数: 2.136
k = 9, 惯性: 2415.63, 轮廓系数: 0.105, CH 指数: 23.18, DB 指数: 2.133
k = 10, 惯性: 2337.41, 轮廓系数: 0.111, CH 指数: 22.31, DB 指数: 2.056

在这里插入图片描述

KMeans Cluster labels (k = 3) added to X:
KMeans_Cluster
0                 108
1                  98
2                  97
Name: count, dtype: int64

X.columns
Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach','exang', 'oldpeak', 'slope', 'ca', 'thal', 'KMeans_Cluster'],dtype='object')

from sklearn.ensemble import RandomForestClassifier
import numpy as np
import shapx1 = X.drop('KMeans_Cluster', axis=1)
y1 = X['KMeans_Cluster']
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(x1, y1)
shap.initjs()
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(x1)
shap_values.shape
(303, 13, 3)

print('SHAP 特征重要性条形图')
shap.summary_plot(shap_values[:, :, 0], x1, plot_type='bar', show=False)
plt.title('SHAP Feature Importance (Bar Plot)')
plt.show()
SHAP 特征重要性条形图

在这里插入图片描述

selected_features = ['slope', 'sex', 'restecg', 'exang']for feature in selected_features:unique_count = X[feature].nunique()print(f'{feature} 的唯一值数量: {unique_count}')if unique_count < 10:print(f'{feature} 可能是离散型变量')else:print(f'{feature} 可能是连续型变量')
slope 的唯一值数量: 3
slope 可能是离散型变量
sex 的唯一值数量: 2
sex 可能是离散型变量
restecg 的唯一值数量: 3
restecg 可能是离散型变量
exang 的唯一值数量: 2
exang 可能是离散型变量

import matplotlib.pyplot as pltfig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.flatten()for i, feature in enumerate(selected_features):axes[i].hist(X[feature], bins=20)axes[i].set_title(f'Histogram of {feature}')axes[i].set_xlabel(feature)axes[i].set_ylabel('Frequency')plt.tight_layout()
plt.show()

在这里插入图片描述

X[['KMeans_Cluster']].value_counts()
KMeans_Cluster
0                 108
1                  98
2                  97
Name: count, dtype: int64

X_cluster0 = X[X['KMeans_Cluster'] == 0]
X_cluster1 = X[X['KMeans_Cluster'] == 1]
X_cluster2 = X[X['KMeans_Cluster'] == 2]
import matplotlib.pyplot as pltfig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.flatten()for i, feature in enumerate(selected_features):axes[i].hist(X_cluster0[feature], bins=20)axes[i].set_title(f'Histogram of {feature}')axes[i].set_xlabel(feature)axes[i].set_ylabel('Frequency')plt.tight_layout()
plt.show()

在这里插入图片描述

import matplotlib.pyplot as pltfig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.flatten()for i, feature in enumerate(selected_features):axes[i].hist(X_cluster1[feature], bins=20)axes[i].set_title(f'Histogram of {feature}')axes[i].set_xlabel(feature)axes[i].set_ylabel('Frequency')plt.tight_layout()
plt.show()

在这里插入图片描述

import matplotlib.pyplot as pltfig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.flatten()for i, feature in enumerate(selected_features):axes[i].hist(X_cluster2[feature], bins=20)axes[i].set_title(f'Histogram of {feature}')axes[i].set_xlabel(feature)axes[i].set_ylabel('Frequency')plt.tight_layout()
plt.show()

在这里插入图片描述
@浙大疏锦行

http://www.cadmedia.cn/news/14791.html

相关文章：

  • 百色建设网站软文营销的案例
  • 宝安区网站建设在线生成个人网站app
  • seo网站建设规划大连头条热点新闻
  • 网站建设的可行性研究的前提html网页制作成品
  • 网站设计建设合同手机网站怎么优化关键词
  • 设备租赁业务网站如何做西安推广平台排行榜
  • 企业网站建设设置那些栏目百度网盟推广官方网站
  • 我想自己创建购物网站夸克浏览器网页版入口
  • 加强政府门户网站建设重要性建网站怎么赚钱
  • 成都网站seo房地产网站模板
  • 网站建设丨下拉找金手指上词快seo网站排名优化教程
  • 局域网内建设网站日本网站源码
  • 网站建设的技术需要微信推广方案
  • 哪个网站做外贸比较好产品推广宣传方案
  • 中山网站建设文化平台手机怎么在百度上发布信息
  • 个人网站制作网站武汉谷歌seo
  • 网站建设服务中企动力网站seo优化工具
  • 动漫网站策划书百度网盘下载安装
  • 福州h5建站郑州网络推广哪个好
  • 腾讯网站建设方案成都网站建设制作公司
  • 应用大全网站百度关键词相关性优化软件
  • 企业网站建设一站通系统简单美橙互联建站
  • 新闻头条最新消息今日头条上海网络排名优化
  • 中国建设银行投标再什么网站上官方百度平台
  • 成都建设二维码网站怎么样优化网站seo
  • 网页版微信消息撤回seo博客优化
  • 营销网站建设多少钱html网页制作软件
  • b2b网站一般流程广州seo招聘网
  • 网站建设有哪些公司发稿网
  • 中国建设银行阜阳分行网站百度搜索关键词规则