1. sklearn简介

sklearn是基于python语言的机器学习工具包，是目前做机器学习项目当之无愧的第一工具。 sklearn自带了大量的数据集，可供我们练习各种机器学习算法。 sklearn集成了数据预处理、数据特征选择、数据特征降维、分类\回归\聚类模型、模型评估等非常全面算法。

2.sklearn数据类型

机器学习最终处理的数据都是数字，只不过这些数据可能以不同的形态被呈现出来，如矩阵、文字、图片、视频、音频等。

3.sklearn总览

数据集

sklearn.datasets

获取小数据集(本地加载)：datasets.load_xxx( )

获取大数据集(在线下载)：datasets.fetch_xxx( )

本地生成数据集(本地构造)：datasets.make_xxx( )

数据集读取的部分代码：

from sklearn import datasets
import matplotlib.pyplot as plt

iris = datasets.load_iris()
features = iris.data
target = iris.target
print(features.shape,target.shape)
print(iris.feature_names)

boston = datasets.load_boston()
boston_features = boston.data
boston_target = boston.target
print(boston_features.shape,boston_target.shape)
print(boston.feature_names)

digits = datasets.load_digits()
digits_features = digits.data
digits_target = digits.target
print(digits_features.shape,digits_target.shape)

img = datasets.load_sample_image('flower.jpg')
print(img.shape)
plt.imshow(img)
plt.show()

data,target = datasets.make_blobs(n_samples=1000,n_features=2,centers=4,cluster_std=1)
plt.scatter(data[:,0],data[:,1],c=target)
plt.show()

data,target = datasets.make_classification(n_classes=4,n_samples=1000,n_features=2,n_informative=2,n_redundant=0,n_clusters_per_class=1)
print(data.shape)
plt.scatter(data[:,0],data[:,1],c=target)
plt.show()

x,y = datasets.make_regression(n_samples=10,n_features=1,n_targets=1,noise=1.5,random_state=1)
print(x.shape,y.shape)
plt.scatter(x,y)
plt.show()

---

数据预处理

sklearn.preprocessing

数据预处理代码

import numpy as np
from sklearn import preprocessing

#标准化：将数据转换为均值为0，方差为1的数据，即标注正态分布的数据
x = np.array([[1,-1,2],[2,0,0],[0,1,-1]])
x_scale = preprocessing.scale(x)
print(x_scale.mean(axis=0),x_scale.std(axis=0))

std_scale = preprocessing.StandardScaler().fit(x)
x_std = std_scale.transform(x)
print(x_std.mean(axis=0),x_std.std(axis=0))

#将数据缩放至给定范围（0-1）
mm_scale = preprocessing.MinMaxScaler()
x_mm = mm_scale.fit_transform(x)
print(x_mm.mean(axis=0),x_mm.std(axis=0))

#将数据缩放至给定范围（-1-1）,适用于稀疏数据
mb_scale = preprocessing.MaxAbsScaler()
x_mb = mb_scale.fit_transform(x)
print(x_mb.mean(axis=0),x_mb.std(axis=0))

#适用于带有异常值的数据
rob_scale = preprocessing.RobustScaler()
x_rob = rob_scale.fit_transform(x)
print(x_rob.mean(axis=0),x_rob.std(axis=0))

#正则化
nor_scale = preprocessing.Normalizer()
x_nor = nor_scale.fit_transform(x)
print(x_nor.mean(axis=0),x_nor.std(axis=0))

#特征二值化：将数值型特征转换位布尔型的值
bin_scale = preprocessing.Binarizer()
x_bin = bin_scale.fit_transform(x)
print(x_bin)

#将分类特征或数据标签转换位独热编码
ohe = preprocessing.OneHotEncoder()
x1 = ([[0,0,3],[1,1,0],[1,0,2]])
x_ohe = ohe.fit(x1).transform([[0,1,3]])
print(x_ohe)


import numpy as np
from sklearn.preprocessing import PolynomialFeatures

x = np.arange(6).reshape(3,2)
poly = PolynomialFeatures(2)
x_poly = poly.fit_transform(x)
print(x)
print(x_poly)

import numpy as np
from sklearn.preprocessing import FunctionTransformer

#自定义的特征转换函数
transformer = FunctionTransformer(np.log1p)

x = np.array([[0,1],[2,3]])
x_trans = transformer.transform(x)
print(x_trans)

import numpy as np
import sklearn.preprocessing

x = np.array([[-3,5,15],[0,6,14],[6,3,11]])
kbd = preprocessing.KBinsDiscretizer(n_bins=[3,2,2],encode='ordinal').fit(x)
x_kbd = kbd.transform(x)
print(x_kbd)

from sklearn.preprocessing import MultiLabelBinarizer

#多标签二值化
mlb = MultiLabelBinarizer()
x_mlb = mlb.fit_transform([(1,2),(3,4),(5,)])
print(x_mlb)

sklearn.svm

上述preprocessing类函数的方法如下：

---

特征选择

很多时候我们用于模型训练的数据集包含许多的特征，这些特征要么是有冗余，要么是对结果的相关性很小；这时通过精心挑选一些"好"的特征来训练模型，既能减小模型训练时间，也能够提升模型性能。

例如一个数据集包含（鼻翼长、眼角长、额头宽、血型）这四个特征；我们用这些数据集进行人脸识别，必定会去除（血型）这个特征后再进行人脸识别；因为（血型）这个特征对于人脸识别这个目标来说是一个无用的特征。

sklean.feature_selection

特征选择实现代码

from sklearn.datasets import load_digits
from sklearn.feature_selection import SelectKBest,chi2

digits = load_digits()
data = digits.data
target = digits.target
print(data.shape)
data_new = SelectKBest(chi2,k=20).fit_transform(data,target)
print(data_new.shape)

from sklearn.feature_selection import VarianceThreshold

x = [[0,0,1],[0,1,0],[1,0,0],[0,1,1],[0,1,0],[0,1,1]]
vt = VarianceThreshold(threshold=(0.8*(1-0.8)))
x_new = vt.fit_transform(x)
print(x)
print(x_new)

from sklearn.svm import LinearSVC
from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectFromModel

iris = load_iris()
x,y = iris.data,iris.target

lsvc = LinearSVC(C=0.01,penalty='l1',dual=False).fit(x,y)
model = SelectFromModel(lsvc,prefit=True)
x_new = model.transform(x)

print(x.shape)
print(x_new.shape)

from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold,cross_val_score
from sklearn.feature_selection import RFECV
from sklearn.datasets import load_iris

iris = load_iris()
x,y = iris.data,iris.target

svc = SVC(kernel='linear')
rfecv = RFECV(estimator=svc,step=1,cv=StratifiedKFold(2),scoring='accuracy',verbose=1,n_jobs=1).fit(x,y)
x_rfe = rfecv.transform(x)
print(x_rfe.shape)

clf = SVC(gamma="auto", C=0.8)   
scores = (cross_val_score(clf, x_rfe, y, cv=5))
print(scores)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std()*2))

特征降维

面对特征巨大的数据集，除了进行特征选择之外，我们还可以采取特征降维算法来减少特征数；特征降维于特征选择的区别在于：特征选择是从原始特征中挑选特征；而特征降维则是从原始特征中生成新的特征。

很多人会有比较特征选择与特征降维优劣的心理，其实这种脱离实际问题的比较意义不大，我们要明白每一种算法都是有其擅长的领域。

sklearn.decomposition

特征降维代码实现

"""数据降维"""

from sklearn.decomposition import PCA

x = np.array([[-1,-1],[-2,-1],[-3,-2],[1,1],[2,1],[3,2]])
pca1 = PCA(n_components=2)
pca2 = PCA(n_components='mle')
pca1.fit(x)
pca2.fit(x)
x_new1 = pca1.transform(x)
x_new2 = pca2.transform(x)
print(x_new1.shape)
print(x_new2.shape)

import numpy as np
from sklearn.decomposition import KernelPCA
import matplotlib.pyplot as plt
import math

#kernelPCA适用于对数据进行非线性降维
x = []
y = []
N = 500

for i in range(N):
    deg = np.random.randint(0,360)
    if np.random.randint(0,2)%2 == 0:
        x.append([6*math.sin(deg),6*math.cos(deg)])
        y.append(1)
    else:
        x.append([15*math.sin(deg),15*math.cos(deg)])
        y.append(0)
        
y = np.array(y)
x = np.array(x)

kpca = KernelPCA(kernel='rbf',n_components=14)
x_kpca = kpca.fit_transform(x)
print(x_kpca.shape)

from sklearn.datasets import load_digits
from sklearn.decomposition import IncrementalPCA
from scipy import sparse
X, _ = load_digits(return_X_y=True)

#增量主成分分析：适用于大数据
transform = IncrementalPCA(n_components=7,batch_size=200)
transform.partial_fit(X[:100,:])

x_sparse = sparse.csr_matrix(X)
x_transformed = transform.fit_transform(x_sparse)
x_transformed.shape

import numpy as np
from sklearn.datasets import make_friedman1
from sklearn.decomposition import MiniBatchSparsePCA

x,_ = make_friedman1(n_samples=200,n_features=30,random_state=0)
transformer = MiniBatchSparsePCA(n_components=5,batch_size=50,random_state=0)
transformer.fit(x)
x_transformed = transformer.transform(x)
print(x_transformed.shape)

from sklearn.datasets import load_digits
from sklearn.decomposition import FactorAnalysis

x,_ = load_digits(return_X_y=True)
transformer = FactorAnalysis(n_components=7,random_state=0)
x_transformed = transformer.fit_transform(x)
print(x_transformed.shape)

sklearn.manifold

---

分类模型

分类模型是能够从数据集中学习知识，进而提升自我认知的一种模型，经过学习后，它能够区分出它所见过的事物；这种模型就非常类似一个识物的小朋友。

sklearn.tree

决策树分类

from sklearn.datasets import load_iris
from sklearn import tree

x,y = load_iris(return_X_y=True)
clf = tree.DecisionTreeClassifier()
clf = clf.fit(x,y)
tree.plot_tree(clf)

sklearn.ensemble

BaggingClassifier

#使用sklearn库实现的决策树装袋法提升分类效果。其中X和Y分别是鸢尾花（iris）数据集中的自变量（花的特征）和因变量（花的类别）

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets

#加载iris数据集
iris=datasets.load_iris()
X=iris.data
Y=iris.target

#生成K折交叉验证数据
kfold=KFold(n_splits=9)

#决策树及交叉验证
cart=DecisionTreeClassifier(criterion='gini',max_depth=2)
cart=cart.fit(X,Y)
result=cross_val_score(cart,X,Y,cv=kfold)  #采用K折交叉验证的方法来验证算法效果
print('CART数结果:',result.mean())

#装袋法及交叉验证
model=BaggingClassifier(base_estimator=cart,n_estimators=100) #n_estimators=100为建立100个分类模型
result=cross_val_score(model,X,Y,cv=kfold)  #采用K折交叉验证的方法来验证算法效果
print('装袋法提升后的结果:',result.mean())

AdaBoostClassifier

#基于sklearn库中的提升法分类器对决策树进行优化，提高分类准确率，其中load_breast_cancer()方法加载乳腺癌数据集，自变量（细胞核的特征）和因变量（良性、恶性）分别赋给X，Y变量

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets

#加载数据
dataset_all=datasets.load_breast_cancer()
X=dataset_all.data
Y=dataset_all.target

#初始化基本随机数生成器
kfold=KFold(n_splits=10)

#决策树及交叉验证
dtree=DecisionTreeClassifier(criterion='gini',max_depth=3)

#提升法及交叉验证
model=AdaBoostClassifier(base_estimator=dtree,n_estimators=100)
result=cross_val_score(model,X,Y,cv=kfold)
print("提升法改进结果:",result.mean())

RandomForestClassifier 、ExtraTreesClassifier

#使用sklearn库中的随机森林算法和决策树算法进行效果比较，数据集由生成器随机生成


from sklearn.model_selection import cross_val_score
from sklearn.datasets import make_blobs
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt

#make_blobs:sklearn中自带的取类数据生成器随机生成测试样本，make_blobs方法中n_samples表示生成的随机数样本数量，n_features表示每个样本的特征数量，centers表示类别数量，random_state表示随机种子
x,y=make_blobs(n_samples=1000,n_features=6,centers=50,random_state=0)
plt.scatter(x[:,0],x[:,1],c=y)
plt.show()

#构造随机森林模型
clf=RandomForestClassifier(n_estimators=10,max_depth=None,min_samples_split=2,random_state=0)  #n_estimators表示弱学习器的最大迭代次数，或者说最大的弱学习器的个数。如果设置值太小，模型容易欠拟合；如果太大，计算量会较大，并且超过一定的数量后，模型提升很小
scores=cross_val_score(clf,x,y)
print('RandomForestClassifier result:',scores.mean())

#构造极限森林模型
clf=ExtraTreesClassifier(n_estimators=10,max_depth=None,min_samples_split=2,random_state=0)
scores=cross_val_score(clf,x,y)
print('ExtraTreesClassifier result:',scores.mean())
#极限随机数的效果好于随机森林，原因在于计算分割点方法中的随机性进一步增强；相较于随机森林，其阈值是针对每个候选特征随机生成的，并且选择最佳阈值作为分割规则，这样能够减少一点模型的方程，总体上效果更好

GradientBoostingClassifier

importpandasaspdfromsklearn.model_selectionimporttrain_test_splitfromsklearn.ensembleimportGradientBoostingClassifierfromsklearn.datasetsimportmake_blobs#make_blobs:sklearn中自带的取类数据生成器随机生成测试样本，make_blobs方法中n_samples表示生成的随机数样本数量，n_features表示每个样本的特征数量，centers表示类别数量，random_state表示随机种子x,y=make_blobs(n_samples=1000,n_features=6,centers=50,random_state=0)plt.scatter(x[:,0],x[:,1],c=y)plt.show()x_train,x_test,y_train,y_test=train_test_split(x,y)# 模型训练，使用GBDT算法gbr=GradientBoostingClassifier(n_estimators=3000,max_depth=2,min_samples_split=2,learning_rate=0.1)gbr.fit(x_train,y_train.ravel())y_gbr=gbr.predict(x_train)y_gbr1=gbr.predict(x_test)acc_train=gbr.score(x_train,y_train)acc_test=gbr.score(x_test,y_test)print(acc_train)print(acc_test)

VotingClassifier

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.ensemble import VotingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

#VotingClassifier方法是一次使用多种分类模型进行预测，将多数预测结果作为最终结果
x,y = datasets.make_moons(n_samples=500,noise=0.3,random_state=42)

plt.scatter(x[y==0,0],x[y==0,1])
plt.scatter(x[y==1,0],x[y==1,1])
plt.show()

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3)

voting_hard = VotingClassifier(estimators=[
    ('log_clf', LogisticRegression()),
    ('svm_clf', SVC()),
    ('dt_clf', DecisionTreeClassifier(random_state=10)),], voting='hard')

voting_soft = VotingClassifier(estimators=[
    ('log_clf', LogisticRegression()),
    ('svm_clf', SVC(probability=True)),
    ('dt_clf', DecisionTreeClassifier(random_state=10)),
], voting='soft')

voting_hard.fit(x_train,y_train)
print(voting_hard.score(x_test,y_test))

voting_soft.fit(x_train,y_train)
print(voting_soft.score(x_test,y_test))

sklearn.linear_model

LogisticRegression

import numpy as np
from sklearn import linear_model,datasets
from sklearn.model_selection import train_test_split

iris = datasets.load_iris()
x = iris.data
y = iris.target

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3)

logreg = linear_model.LogisticRegression(C=1e5)
logreg.fit(x_train,y_train)

prepro = logreg.score(x_test,y_test)
print(prepro)

Perceptron

from sklearn.datasets import load_digits
from sklearn.linear_model import Perceptron

x,y = load_digits(return_X_y=True)
clf = Perceptron(tol=1e-3,random_state=0)
clf.fit(x,y)
clf.score(x,y)

SGDClassifier

import numpy as np
from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline

x = np.array([[-1,-1],[-2,-1],[1,1],[2,1]])
y = np.array([1,1,2,2])

clf = make_pipeline(StandardScaler(),SGDClassifier(max_iter=1000,tol=1e-3))
clf.fit(x,y)
print(clf.score(x,y))
print(clf.predict([[-0.8,-1]]))

PassiveAggressiveClassifier

from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

x,y = make_classification(n_features=4,random_state=0)
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3)

clf = PassiveAggressiveClassifier(max_iter=1000,random_state=0,tol=1e-3)
clf.fit(x_train,y_train)
print(clf.score(x_test,y_test))

sklearn.svm

SVC

import numpy as np
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC

x = [[2,0],[1,1],[2,3]]
y = [0,0,1]

clf = SVC(kernel='linear')
clf.fit(x,y)
print(clf.predict([[2,2]]))

NuSVC

from sklearn import svm
from numpy import *

x = array([[0],[1],[2],[3]])
y = array([0,1,2,3])

clf = svm.NuSVC()
clf.fit(x,y)
print(clf.predict([[4]]))

LinearSVC

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import LinearSVC

iris = datasets.load_iris()
X = iris.data
y = iris.target

plt.scatter(X[y==0, 0], X[y==0, 1], color='red')
plt.scatter(X[y==1, 0], X[y==1, 1], color='blue')
plt.show()

svc = LinearSVC(C=10**9)
svc.fit(X, y)
print(svc.score(X,y))

sklearn.neighbors

NearestNeighbors

import numpy as np
from sklearn.neighbors import NearestNeighbors

samples = [[0,0,2],[1,0,0],[0,0,1]]
neigh = NearestNeighbors(n_neighbors=2,radius=0.4)
neigh.fit(samples)

print(neigh.kneighbors([[0,0,1.3]],2,return_distance=True))
print(neigh.radius_neighbors([[0,0,1.3]],0.4,return_distance=False))

NearestCentroid

from sklearn.neighbors import NearestCentroid
import numpy as np

x = np.array([[-1,-1],[-2,-1],[-3,-2],[1,1],[2,1],[3,2]])
y = np.array([1,1,1,2,2,2])

clf = NearestCentroid()
clf.fit(x,y)
print(clf.predict([[-0.8,-1]]))

KNeighborsClassifier

from sklearn.neighbors import KNeighborsClassifier

x,y = [[0],[1],[2],[3]],[0,0,1,1]

neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(x,y)
print(neigh.predict([[1.1]]))

KDTree

import numpy as np
from sklearn.neighbors import KDTree
rng = np.random.RandomState(0)
x = rng.random_sample((10,3))
tree = KDTree(x,leaf_size=2)
dist,ind = tree.query(x[:1],k=3)
print(ind)

KNeighborsClassifier

from sklearn.neighbors import KNeighborsClassifier
 
X = [[0], [1], [2], [3], [4], [5], [6], [7], [8]]
y = [0, 0, 0, 1, 1, 1, 2, 2, 2]
 
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(X, y)
print(neigh.predict([[1.1]]))

sklearn.discriminant_analysis

LDA

from sklearn import datasets
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA

iris = datasets.load_iris()
X = iris.data[:-5]
pre_x = iris.data[-5:]
y = iris.target[:-5]
print ('first 10 raw samples:', X[:10])
clf = LDA()
clf.fit(X, y)
X_r = clf.transform(X)
pre_y = clf.predict(pre_x)
#降维结果
print ('first 10 transformed samples:', X_r[:10])
#预测目标分类结果
print ('predict value:', pre_y)

QDA

from sklearn import datasets
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA
from sklearn.model_selection import train_test_split

iris = datasets.load_iris()

x = iris.data
y = iris.target

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3)

clf = QDA()
clf.fit(x_train,y_train)
print(clf.score(x_test,y_test))

sklearn.gaussian_process

sklearn.naive_bayes

GaussianNB

from sklearn import datasets
from sklearn.naive_bayes import GaussianNB

iris = datasets.load_iris()
clf = GaussianNB()
clf = clf.fit(iris.data,iris.target)

y_pre = clf.predict(iris.data)

MultinomialNB

from sklearn import datasets
from sklearn.naive_bayes import MultinomialNB

iris = datasets.load_iris()
clf = MultinomialNB()
clf = clf.fit(iris.data, iris.target)
y_pred=clf.predict(iris.data)

BernoulliNB

from sklearn import datasets
from sklearn.naive_bayes import BernoulliNB

iris = datasets.load_iris()
clf = BernoulliNB()
clf = clf.fit(iris.data, iris.target)
y_pred=clf.predict(iris.data)

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回归模型

sklearn.tree

DecisionTreeRegressor、ExtraTreeRegressor

"""回归"""

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor,ExtraTreeRegressor
from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error
import numpy as np

boston = load_boston()
x = boston.data
y = boston.target

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3)

dtr = DecisionTreeRegressor()
dtr.fit(x_train,y_train)

etr = ExtraTreeRegressor()
etr.fit(x_train,y_train)

yetr_pred = etr.predict(x_test)
ydtr_pred = dtr.predict(x_test)

print(dtr.score(x_test,y_test))
print(r2_score(y_test,ydtr_pred))

print(etr.score(x_test,y_test))
print(r2_score(y_test,yetr_pred))

sklearn.ensemble

GradientBoostingRegressor

import numpy as np
from sklearn.ensemble import GradientBoostingRegressor as GBR
from sklearn.datasets import make_regression

X, y = make_regression(1000, 2, noise=10)

gbr = GBR()
gbr.fit(X, y)
gbr_preds = gbr.predict(X)

AdaBoostRegressor

from sklearn.ensemble import AdaBoostRegressor
from sklearn.datasets import make_regression

x,y = make_regression(n_features=4,n_informative=2,random_state=0,shuffle=False)
regr = AdaBoostRegressor(random_state=0,n_estimators=100)
regr.fit(x,y)
regr.predict([[0,0,0,0]])

BaggingRegressor

from sklearn.ensemble import BaggingRegressor
from sklearn.datasets import make_regression
from sklearn.svm import SVR

x,y = make_regression(n_samples=100,n_features=4,n_informative=2,n_targets=1,random_state=0,shuffle=False)
br = BaggingRegressor(base_estimator=SVR(),n_estimators=10,random_state=0).fit(x,y)
br.predict([[0,0,0,0]])

ExtraTreesRegressor

from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from sklearn.ensemble import ExtraTreesRegressor

x,y = load_diabetes(return_X_y=True)
x_train,x_test,y_train,y_test = train_test_split(X,y,random_state=0)

etr = ExtraTreesRegressor(n_estimators=100,random_state=0).fit(x_train,y_train)
print(etr.score(x_test,y_test))

RandomForestRegressor

from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression

x,y = make_regression(n_features=4,n_informative=2,random_state=0,shuffle=False)

rfr = RandomForestRegressor(max_depth=2,random_state=0)
rfr.fit(x,y)
print(rfr.predict([[0,0,0,0]]))

sklearn.linear_model

import numpy as np
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso

np.random.seed(0)
x = np.random.randn(10,5)
y = np.random.randn(10)
clf1 = Ridge(alpha=1.0)
clf2 = Lasso()
clf2.fit(x,y)
clf1.fit(x,y)
print(clf1.predict(x))
print(clf2.predict(x))

sklearn.svm

sklearn.neighbors

sklearn.kernel_ridge

sklearn.gaussian_process

GaussianProcessRegressor

from sklearn.datasets import make_friedman2
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import DotProduct,WhiteKernel

x,y = make_friedman2(n_samples=500,noise=0,random_state=0)

kernel = DotProduct()+WhiteKernel()
gpr = GaussianProcessRegressor(kernel=kernel,random_state=0).fit(x,y)
print(gpr.score(x,y))

sklearn.cross_decomposition

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cross_decomposition import PLSRegression
from sklearn.model_selection import train_test_split

boston = datasets.load_boston()

x = boston.data
y = boston.target

x_df = pd.DataFrame(x,columns=boston.feature_names)
y_df = pd.DataFrame(y)

pls = PLSRegression(n_components=2)

x_train,x_test,y_train,y_test = train_test_split(x_df,y_df,test_size=0.3,random_state=1)

pls.fit(x_train,y_train)
print(pls.predict(x_test))

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聚类模型

sklearn.cluster

模型方法

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模型评估

分类模型评估

回归模型评估

聚类模型评估

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模型优化

写在最后

单纯的通过文章来学习机器学习、学习编程是很容易遇到非常多的Bug，这对一个新手来说，无疑会浪费很多时间，也会打击大家学习掌握机器学习的信心。

利用sklearn库来学习机器学习，是能够非常快速的入门的。