sklearn；python
關(guān)注創(chuàng)建者：博集華仿 創(chuàng)建時間：2019-12-13
sklearn；python的實例教程
摘要：本文主要展示樸素貝葉斯模型在分類中的使用，包含三種：高斯貝葉斯，多項式貝葉斯，伯努利貝葉斯（二項貝葉斯）；
00 安裝scikit-learn庫
pip install scikit-learn
01 獲取sklearn圖像識別素材
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, naive_bayes
digits=datasets.load_digits()
dex1=np.random.choice(1797,1500,replace=False)
dex2=[]
for i in range(1797):
if i not in dex1:
dex2.append(i)
train_x=digits.data[dex1]
train_y=digits.target[dex1]
test_x=digits.data[dex2]
test_y=digits.target[dex2]
02 高斯貝葉斯模型
classi=naive_bayes.GaussianNB()
classi.fit(train_x,train_y)
classi.score(test_x,test_y)
Out[44]: 0.835016835016835
classi.predict(test_x)
Out[45]:
array([0, 7, 1, 3, 7, 5, 4, 7, 8, 0, 1, 6, 3, 3, 4, 5, 1, 7, 7, 5, 8, 8,
1, 8, 3, 6, 8, 0, 7, 7, 3, 8, 7, 1, 3, 7, 3, 9, 0, 6, 9, 7,
展開 in weights:
scor=[]
for k in ks:
regre=neighbors.KNeighborsRegressor(weights=weight,n_neighbors=k)
regre.fit(train_x,train_y)
scor.append(regre.score(test_x,test_y))
plt.plot(ks,scor,label=weight)
plt.legend()
fig=plt.figure()
ps=[1,2,10]
ks=np.linspace(1,len(train_y),50,dtype='int')
for p in ps:
scor=[]
for k in ks:
regre=neighbors.KNeighborsRegressor(p=p,n_neighbors=k)
regre.fit(train_x,train_y)
scor.append(regre.score(test_x,test_y))
plt.plot(ks,scor,label='p='+str(p))
plt.legend(loc='best')
02 獲取sklearn中數(shù)據(jù)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets,neighbors
digits=datasets.load_digits()
dex1=np.random.choice(1797,1500,replace=False)
dex2=[]
for
展開 00 安裝scikit-learn庫
pip install scikit-learn
01 獲取sklearn中鳶尾花數(shù)據(jù)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
iris=datasets.load_iris()
dex1=np.random.choice(150,size=120,replace=False)
dex2=[]
for i in range(150):
if i not in dex1:
dex2.append(i)
train_x=iris.data[dex1,:]
train_y=iris.target[dex1]
test_x=iris.data[dex2,:]
test_y=iris.target[dex2]
02 分類樹
classi=DecisionTreeClassifier()
classi.fit(train_x,train_y)
classi.score(test_x,test_y)
Out[112]: 0.9333333333333333
classi.predict(test_x)
classi=DecisionTreeClassifier(criterion='gini')
classi.fit(train_x,train_y)
classi.score(test_x,test_y)
Out[135]: 0.9
classi=DecisionTreeClassifier(criterion='entropy')
classi.fit
展開 摘要：本文主要展示scikit-learn中關(guān)于線性回歸模型的使用，涉及到線性回歸，嶺回歸，Lasso回歸，ElasticNet回歸；
00 安裝scikit-learn庫
pip install scikit-learn
01 獲取sklearn中糖尿病患者數(shù)據(jù)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
diabetes=datasets.load_diabetes()
train_x=diabetes.data[:342,:]
train_y=diabetes.target[:342].reshape(-1,1)
test_x=diabetes.data[342:,:]
test_y=diabetes.target[342:].reshape(-1,1)
02 線性回歸
regre=linear_model.LinearRegression()
regre.fit(train_x,train_y)
獲取回歸模型參數(shù)：
regre.coef_
Out[16]:
array([[ -8.41868608, -246.87356823, 515.48111342, 302.57455473,
-403.03503059, 160.72498918, -73.58961448, 127.08804647,
609.91854338, 87.69439173]])
regre.intercept_
Out[17]: array([152.09317069])
regre.score(test_x,test_y)
Out
展開 
sklearn；python的相關(guān)專題、標(biāo)簽、搜索
sklearn；python的最新內(nèi)容
摘要：本文使用K近鄰模型進(jìn)行回歸，分類；
00 構(gòu)造數(shù)據(jù)
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from sklearn import neighbors
x=np.pi*2*np.random.rand(100)
y=np.sin(x)
y[::5]+=(np.random.rand
摘要：本文主要展示樸素貝葉斯模型在分類中的使用，包含三種：高斯貝葉斯，多項式貝葉斯，伯努利貝葉斯（二項貝葉斯）；
00 安裝scikit-learn庫
pip install scikit-learn
01 獲取sklearn圖像識別素材
import numpy as np
import matplotlib.pyplot as plt
from sklearn import
摘要：本文使用決策樹進(jìn)行分類，回歸。
00 安裝scikit-learn庫
pip install scikit-learn
01 獲取sklearn中鳶尾花數(shù)據(jù)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.tree
摘要：本文主要展示scikit-learn中關(guān)于線性回歸模型的使用，涉及到線性回歸，嶺回歸，Lasso回歸，ElasticNet回歸；
00 安裝scikit-learn庫
pip install scikit-learn
01 獲取sklearn中糖尿病患者數(shù)據(jù)
import numpy as np
import matplotlib.pyplot as plt
from
