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本人很懒没有整理参考来源，“你们都别看”系列是个人学习笔记，几乎都是在别人代码的基础上自己稍作修改，方便二次利用，介意的话请跳过这个系列文章，谢谢参考至《Python大战机器学习》基础篇线性模型 线性回归模型 线性回归模型的正则化 逻辑回归 线性判别分析 决策树 回归决策树（DecisionTreeRegressor） 分类决策树（DecisionTreeClassifir...

模型原型 sklearn.svm.LinearSVC(penalty=’l2’,loss=’squared_hinge’,dual=True,tol=0.0001,C=1.0,multi_class=’ovr’, fit_intercept=True,intercept_scaling=1,class_weight=None,verbose=0,random_state=None,max_ite...

模型原型 class sklearn.tree.DecisionTreeRegressor(criterion=’mse’,splitter=’best’,max_depth=None, min_samples_samples_split=2,min_samples_leaf=1,min_weight_fraction_leaf=0.0, max_features=None,random_s...

模型原型 class sklearn.linear_model.LinearRegression(fit_intercept=True,normalisze=False, copy_X=True,n_jobs=1) 参数fit_intercept:是否计算b值normalisze:回归前是否归一化copy_X:是否复制Xn_jobs:任务并行时指定的CPU数量（-1表示使用所有可...

import matplotlib.pyplot as pltimport numpy as npfrom sklearn import datasets,linear_model,discriminant_analysis,cross_validation加载数据def load_data(): diabetes=datasets.load_diabetes() ...

模型原型 class sklearn.linear_model.LogisticRegression(penalty=’l2’,dual=False,tol=0.0001,C=1.0,fit_intercept=True,intercept_scaling=1, class_weight=None,random_state=None,solver=’liblinear’,max_iter=10...

模型原型 class sklearn.decomposition.PCA(n_components=None,whiten=False,copy=True,batch_size=None) 参数 n_components whiten copy batch_size:指定每个批次训练时，使用的样本数量(当调用fit()/partial_fit()方法时会用到该参数) 属性 compo

模型原型 class sklearn.decomposition.PCA(n_components=None,copy=True, whiten=False) 参数n_components:降维后的维数 None:min(n_samples,n_features)‘mle’:使用Minka’s MLE算法来猜测降维后的维数大于0、小于1的浮点数:降维后的维数占原始维数的百...

模型原型 sklearn.neighbors.KNeighborsRegressor(n_neighbors=5,weights=’uniform’, algorithm=’auto’,leaf_size=30,p=2, metric=’minkowski’,metric_params=None,n_jobs=1,**kwargs) 参数n_neighborsweightsalgo...

模型原型 sklearn.neighbors.KNeighborsClassifier(n_neighbors=5,weights=’uniform’,algorithm=’auto’,leaf_size=30,p=2, metric=’minkowski’,metric_params=None,n_jobs=1,**kwargs) 参数n_neighbors:k值weights:投票...

模型原型 partial_fit(X,y,classes=None,sample_weight=None) 参数X:样本数据y:样本标记classes:列出所有可能的类别sample_weight:给出每个样本的权重(未指定，则全为1)(使用该方法时，最好每次数据块都足够大，推荐每次填满整个内存，通过连续调用partial_fit方法，成百上千GB的数据集就可以被切成一块一块地来进行训

分类问题中的性能度量accuracy_scorefrom sklearn.metrics import accuracy_scorey_true=[1,1,1,1,1,0,0,0,0,0]y_pred=[0,0,1,1,0,0,1,1,0,0]print('Accuracy Score(normalize=True):%s\nAccuracy Score(normalize=True):%s'

模型原型class sklearn.preprocessing.Binarizer(threshold=0.0,copy=True) 参数threshold:指定属性阙值copy:如果为True，则执行原地修改方法fit(X,[,y]):不做任何事，主要用于流水线Pipelinetransform(X[,y,copy]):将每一个样本的属性二元化fit_transform(X[,y])

0-1损失函数模型原型sklearn.metrics.zero_one_loss(y_true, y_pred, normalize=True, sample_weight=None)参数y_true :样本集中每个样本对应的真实值y_pred :学习器对样本集中每个样本的预测的预测值normalize True:返回分类样本的比例False:返回分类样本的数量sample_weig

暴力搜索寻优GridSearchCVfrom sklearn.datasets import load_digitsfrom sklearn.linear_model import LogisticRegressionfrom sklearn.model_selection import GridSearchCVfrom sklearn.metrics import classificatio

train_test_split模型原型class sklearn.model_selection.train_test_split(arrays, *options) 参数*arrays : 一个或多个数据集test_size : 指定测试集的大小 浮点数：测试集占原始数据集的比例整数：测试集的大小None：测试集大小=原始数据集大小-训练数据集大小train_size : 指定训练

模型原型 class sklearn.naive_bayes.BernoulliNB(alpha=1.0,binarize=0.0,fit_prior=True,class_prior=None) 参数 alpha binarize:None:假定原始数据已经二元化 浮点数:以该数值为界fit_prior class_prior:指定每个分类的先验概率 属性class_...

模型原型 class sklearn.naive_bayes.MultinomialNB(alpha=1.0,fit_prior=True,class_prior=None) 参数alphafit_prior True:均匀分布True:均匀分布 True:均匀分布 False:P(y=ck)False:P(y=ck) False:P(y=c_k) class_pr...

模型原型 sklearn.neighbors.KMeans(n_clusters=8,init=’k_means++’,n_init=10,max_iter=300,tol=0.0001, precompute_distances=’auto’,verbose=0,random_state=None,copy_x=True) 参数n_clusters:分类簇的数量init:指定初始均值向量的

模型原型class sklearn.semi_supervised.LabelSpreading(kernel=’rbf’,gamma=20,n_neighbors=7,alpha=0.2,max_iter=30,tol=0.001) 参数kernelgamman_neighborsalphamax_itertol属性X_classes_labeldistributionst

模型原型class sklearn.semi_supervised.LabelPropagation(kernel=’rbf’,gamma=20,n_neighbors=7,alpha=1,max_iter=30,tol=0.001) 参数kernel:指定核函数（距离函数） ’rbf’‘knn’gamma:为rbf核的参数n_neighbors:为knn核的参数alphamax_i

from matplotlib import pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dimport numpy as npfrom sklearn.neural_network import MLPClassifierfrom sklearn.datasets import load_iris生成线性可分数据集def ...

from matplotlib import pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dimport numpy as npfrom sklearn.neural_network import MLPClassifierfrom sklearn.datasets import load_iris生成线性可分数据集def creat

from matplotlib import pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dimport numpy as npfrom sklearn.neural_network import MLPClassifierfrom sklearn.datasets import load_iris生成线性可分数据集de...

from matplotlib import pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dimport numpy as npfrom sklearn.neural_network import MLPClassifierfrom sklearn.datasets import load_iris生成线性不可分数据集def crea

模型原型 sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(100,),activation=’relu’, algorithm=’adam’,alpha=0.0001,batch_size=’auto’,learning_rate=’constant’, learning_rate_init=0.001,power_t=0.5,

from matplotlib import pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dimport numpy as npfrom sklearn.neural_network import MLPClassifierfrom sklearn.datasets import load_iris生成线性不可分数据集def crea

加载数据import numpy as npimport matplotlib.pyplot as pltfrom matplotlib.colors import ListedColormapfrom sklearn import neighbors,datasetsnp.random.seed(0)iris=datasets.load_iris()X=iris.data[:,0:2]

模型原型 sklearn.svm.SVC(C=1.0,kernel=’rbf’,degree=3,gamma=’auto’,coef0=0.0,shrinking=True,probability=False,tol=0.001,cache_size=200, class_weight=None,verbose=False,max_iter=-1,decision_function_shape=N

import matplotlib.pyplot as pltimport numpy as npfrom sklearn import datasets,linear_model,cross_validation,svm加载数据def load_data_regression(): diabetes=datasets.load_diabetes() return cross_v

import matplotlib.pyplot as pltimport numpy as npfrom sklearn import datasets,cross_validation,ensemble加载数据集#糖尿病病人def load_data_regression(): diabetes=datasets.load_diabetes() return cross_v

import matplotlib.pyplot as pltimport numpy as npfrom sklearn import datasets,cross_validation,ensemble加载数据集#糖尿病病人def load_data_regression(): diabetes=datasets.load_diabetes() return cross_v

模型原型 class sklearn.decomposition.KernelPCA(n_components=None,kernel=’linear’,gamma=None,degress=3,coef0=1,kernel_params=None,alpha=1.0,fit_inverse_transform=False,eigen_solver=’auto’,tol=0,max_iter=No

模型原型 class sklearn.manifold.MDS(n_components=2,metric=True,n_init=4,max_iter=300,verbose=0,eps=0.001,n_jobs=1,random_state=None,dissimilarity=’euclidean’) 参数n_components:低维空间metric: True:距离度量F...

模型原型 class sklearn.manifold.Isomap(n_neighbors=5,n_components=2,eigen_solver=’auto’,tol=0,max_iter=300,path_method=’auto’,neighbors_algorithm=’auto’) 参数n_neighbors:近邻参数kn_components:指定低维的维数eigen_s

模型原型 class sklearn.manifold.LocallyLinearEmbedding(n_neighbors=5,n_components=2,reg=0.001,eigen_solver=’auto’,tol=0,max_iter=300, method=’standard’,hessian_tol=0.0001,modified_tol=1e.12, neighbors_alg

模型原型 参数——没有参数 class sklearn.naive_bayes.GaussianNB 属性classpriorclasscounttheta_sigma_方法fit(X,y[,sample_weight])partial_fix(X,y[,classes,sample_weight]):追加训练模型(用于大规模数据集，可将大数据集划分为若干个小数据集...

from sklearn.tree import DecisionTreeClassifierfrom sklearn.tree import export_graphvizfrom sklearn import datasetsfrom sklearn import cross_validationimport pydotdef load_data(): iris=datasets

模型原型 class sklearn.tree.DecisionTreeClassifier(criterion=’gini’,splitter=’best’, max_depth=None,min_samples_samples_split=2,min_samples_leaf=1, min_weight_fraction_leaf=0.0,max_features=None,random...

模型原型 class sklearn.cluster.DBSCAN(eps=0.5,min_samples=5,metric=’euclidean’, algorithm=’auto’,leaf_size=30, p=None,random_state=None) 参数 - eps:ϵ参数，用于确定邻域大小 - min_samples:MinPts参数，用于判断核心对象 - met