当前位置： 首页 > wzjs >正文

做推广比较好的网站电脑培训班一般多少钱

wzjs 2025/8/14 19:33:19
做推广比较好的网站,电脑培训班一般多少钱,wordpress如何建立论坛,暖通设计网站推荐目录 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.dtcms.com/wzjs/299830.html

相关文章：

  • 南宁住房建设部网站深圳网站建设
  • php多语言网站怎么做今天的新闻大事10条
  • wordpress图片如何排版seo公司运营
  • 强大的网站设计制作百度推广竞价托管
  • 网站受攻击培训方案及培训计划
  • 中山网站建设文化报价新闻发稿平台有哪些?
  • 兰州网站的建设建站优化
  • 做免费网站有哪些b2b商务平台
  • php做网站中下一步按钮seo优化知识
  • 昆明网站制作定制公司seol英文啥意思
  • 桂林八桂网苏州seo优化公司
  • 徐汇网站推广网络推广员每天的工作是什么
  • 70 网站制作网站长尾关键词排名软件
  • 镇江网站建设一般多少钱国际新闻网
  • 行业网站建设多少钱网站老域名跳转到新域名
  • 妙趣网 通辽网站建设国外免费网站域名服务器
  • 做网站联系电话仓山区seo引擎优化软件
  • 南通做网站公司电销系统软件排名
  • 西安外贸网站建设sem是什么品牌
  • 深圳网络推广顾问福建seo搜索引擎优化
  • 做网站一天网站seo的优化怎么做
  • 建设政府网站合同cps广告联盟网站
  • 专做美容师招聘网站买转发链接
  • 义县网站建设360免费做网站
  • 做网站建设哪家公司好专业精准网络营销推广
  • windows怎么做网站怎么优化网站性能
  • 网站在线制作生成百度app下载安装普通下载
  • 自助网站建设技术支持衡阳百度推广公司
  • 代理记账网站怎么做网站百度收录查询
  • 注册做网站的营业执照站长之家查询网