随机森林乳腺癌案例

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.ensemble import RandomForestClassifier
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

导入数据集，探索数据

cancer = load_breast_cancer()
cancer.data.shape

(569, 30)

进行一次简单的建模，看看模型本身在数据集上的效果

rfc = RandomForestClassifier(n_estimators=100, random_state=90)
score_pre = cross_val_score(rfc, cancer.data, cancer.target, cv=10).mean()
score_pre

0.9648809523809524

随机森林调整的第一步：无论如何先来调 n_estimators

score_ = []
for i in range(0,200,10):
    rfc = RandomForestClassifier(n_estimators=i+1, random_state=90)
    score = cross_val_score(rfc, cancer.data, cancer.target, cv=10).mean()
    score_.append(score)

查看 n_estimators（1-200）最高得分

print(max(score_), (score_.index(max(score_)) * 10) + 1)

0.9631265664160402 71

nestimators 学习曲线

plt.figure(figsize=(20, 8),dpi=80)
plt.plot(range(1,201,10), score_)
plt.show()

细化 n_estimators 取值

est_range = [*range(65,75)]
est_refinement = []
for i in est_range:
    rfc = RandomForestClassifier(n_estimators=i, random_state=90)
    score = cross_val_score(rfc, cancer.data, cancer.target, cv=10).mean()
    est_refinement.append(score)
print(max(est_refinement), est_range[est_refinement.index(max(est_refinement))])

0.9666353383458647 73

为网格搜索做准备，书写网格搜索的参数

"""
param_grid = {'n_estimators':np.arange(0, 200, 10)}
 
param_grid = {'max_depth':np.arange(1, 20, 1)}
    
param_grid = {'max_leaf_nodes':np.arange(25,50,1)}
    对于大型数据集，可以尝试从1000来构建，先输入1000，每100个叶子一个区间，再逐渐缩小范围
 
有一些参数是可以找到一个范围的，或者说我们知道他们的取值和随着他们的取值，模型的整体准确率会如何变化，这
样的参数我们就可以直接跑网格搜索
param_grid = {'criterion':['gini', 'entropy']}
 
param_grid = {'min_samples_split':np.arange(2, 2+20, 1)}
 
param_grid = {'min_samples_leaf':np.arange(1, 1+10, 1)}
    
param_grid = {'max_features':np.arange(5,30,1)} 
 
"""

"\nparam_grid = {'n_estimators':np.arange(0, 200, 10)}\n \nparam_grid = {'max_depth':np.arange(1, 20, 1)}\n \nparam_grid = {'max_leaf_nodes':np.arange(25,50,1)}\n 对于大型数据集，可以尝试从1000来构建，先输入1000，每100个叶子一个区间，再逐渐缩小范围\n \n有一些参数是可以找到一个范围的，或者说我们知道他们的取值和随着他们的取值，模型的整体准确率会如何变化，这\n样的参数我们就可以直接跑网格搜索\nparam_grid = {'criterion':['gini', 'entropy']}\n \nparam_grid = {'min_samples_split':np.arange(2, 2+20, 1)}\n \nparam_grid = {'min_samples_leaf':np.arange(1, 1+10, 1)}\n \nparam_grid = {'max_features':np.arange(5,30,1)} \n \n"

开始按照参数对模型整体准确率的影响程度进行调参，首先调整 max_depth

param_grid = {'max_depth':np.arange(1, 20, 1)}
rfc = RandomForestClassifier(n_estimators=73, random_state=90)
grid = GridSearchCV(rfc, param_grid,cv=10)
grid.fit(cancer.data, cancer.target)

GridSearchCV(cv=10,
estimator=RandomForestClassifier(n_estimators=73, random_state=90),

param_grid={'max_depth': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,

18, 19])})

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

# 查看最好模型参数
grid.best_params_

{'max_depth': 8}

# 查看最好的分
grid.best_score_

0.9666353383458647

调整 max_features

param_grid = {'max_features':np.arange(5,30,1)} 
rfc = RandomForestClassifier(n_estimators=73, random_state=90)
grid = GridSearchCV(rfc, param_grid,cv=10)
grid.fit(cancer.data, cancer.target)

GridSearchCV(cv=10,
estimator=RandomForestClassifier(n_estimators=73, random_state=90),

param_grid={'max_features': array([ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,

22, 23, 24, 25, 26, 27, 28, 29])})

# 查看最好模型参数
grid.best_params_

{'max_features': 24}

# 查看最好的分
grid.best_score_

0.9666666666666668

调整 min_samples_leaf

param_grid = {'min_samples_leaf':np.arange(1, 1+10, 1)} 
rfc = RandomForestClassifier(n_estimators=73, random_state=90)
grid = GridSearchCV(rfc, param_grid,cv=10)
grid.fit(cancer.data, cancer.target)

GridSearchCV(cv=10,
estimator=RandomForestClassifier(n_estimators=73, random_state=90),

param_grid={'min_samples_leaf': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])})

# 查看最好模型参数
grid.best_params_

{'min_samples_leaf': 1}

# 查看最好的分
grid.best_score_

0.9666353383458647

不懈努力，继续尝试 min_samples_split

param_grid = {'min_samples_split':np.arange(2, 2+20, 1)}
rfc = RandomForestClassifier(n_estimators=73, random_state=90)
grid = GridSearchCV(rfc, param_grid,cv=10)
grid.fit(cancer.data, cancer.target)

GridSearchCV(cv=10,
estimator=RandomForestClassifier(n_estimators=73, random_state=90),

param_grid={'min_samples_split': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,

19, 20, 21])})

# 查看最好模型参数
grid.best_params_

{'min_samples_split': 2}

# 查看最好的分
grid.best_score_

0.9666353383458647

最后尝试一下 criterion

param_grid = {'criterion':['gini', 'entropy']}
rfc = RandomForestClassifier(n_estimators=73, random_state=90)
grid = GridSearchCV(rfc, param_grid,cv=10)
grid.fit(cancer.data, cancer.target)

GridSearchCV(cv=10,
estimator=RandomForestClassifier(n_estimators=73, random_state=90),

param_grid={'criterion': ['gini', 'entropy']})

# 查看最好模型参数
grid.best_params_

{'criterion': 'gini'}

# 查看最好的分
grid.best_score_

0.9666353383458647

最好模型参数

rfc = RandomForestClassifier(n_estimators=73, 
                             random_state=90, 
                             max_features=24)
score = cross_val_score(rfc, cancer.data, cancer.target, cv=10).mean()
score

0.9666666666666668

最终提高准确率

score - score_pre

0.0017857142857143904

posted @ 2023-04-06 22:39 ThankCAT 阅读(98) 评论(0) 收藏举报

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随机森林乳腺癌案例

随机森林在乳腺癌数据上的调参

导入需要的库

导入数据集，探索数据

进行一次简单的建模，看看模型本身在数据集上的效果

随机森林调整的第一步：无论如何先来调 n_estimators

查看 n_estimators（1-200）最高得分

nestimators 学习曲线

细化 n_estimators 取值

为网格搜索做准备，书写网格搜索的参数

开始按照参数对模型整体准确率的影响程度进行调参，首先调整 max_depth

调整 max_features

调整 min_samples_leaf

不懈努力，继续尝试 min_samples_split

最后尝试一下 criterion

最好模型参数

最终提高准确率

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