andrew ng machine learning python

) p (CI)/P (W)Calculate a specific document W belongs to C0 (insulting document) or C1 (non-insulting document), statistics the probability of each word in this document in two different categories, quantified by the Bayesian formula, that is, each word in a particular document in the p0v or p1v to find the corresponding word probability, Multiply these probabilities, i.e. P (W0|CI) p (W1|CI) p (w2|ci). P (WN|CI), multiplied by P (CI), the final result is two probability values, the probability

[21]): Errorcount + = 1 #计算错误率 errorrate = (Float (errorcou NT)/numtestvec) print "The error rate of this test is:%f"% errorrate return errorratedef multitest (): numtests = 10; errorsum=0.0 for K in range (numtests): Errorsum + = Colictest () print "After%d iterations the average error R ATE is:%f "% (numtests, errorsum/float (numtests))Implementation results:The error rate of this test is:0.358209the error rate of this test is:0.417910the error rate of this test is:0.268657th E error r

# like random forests, tree-based decision trees are built in a continuous way, with a very small depth of max_depthFrom sklearn.ensemble import GradientboostingclassifierFrom sklearn.datasets import Load_breast_cancerFrom sklearn.model_selection import Train_test_splitCancer=load_breast_cancer ()X_train,x_test,y_train,y_test=train_test_split (cancer.data,cancer.target,random_state=0)Gbrt=gradientboostingclassifier () #模型不做参数调整Gbrt.fit (X_train,y_train)Print (Gbrt.score (x_train,y_train))Print (

See original book 2.1-2.2 sectionThe new dataset is like a wrapped gift, filled with promise and hope!But until you open it, it remains mysterious!I. Structure and terminology of the underlying problem, characteristics of the machine learning data setTypically, rows represent instances, columns represent attribute characteristicsproperty, the data used in the instance for predictionOther Name: Predictive fa

[0]print ("k=", K, "b = ", b) Print (" Cost: "+str (para[1)) print (" Solved fit line is: ") print (" y= "+str (rOund (k,2)) + "x+" +str (Round (b,2)) "'" plot to see the fit effect. Matplotlib default does not support Chinese, label set Chinese words need to be set separately if the error, change into English can be "#画样本点plt. Figure (Figsize= (8,6)) # #指定图像比例: 8:6plt.scatter (Xi,yi, Color= "Green", label= "Sample Data", linewidth=2) #画拟合直线x =np.linspace (0,12,100) # #在0-15 Direct Draw 100 cons

') trainset= []; Trainlabels = [] forLineinchtrainfile.readlines (): line_s= Line.strip (). Split ('\ t') Linearr= [] forIinchRange (21): Linearr.append (float (line_s[i)) trainset.append (Linearr) trainlabels.append (float (line_s[ -1])) Sigma= StocGradAscent1 (trainset,trainlabels,500) error_cnt=0.0;numtestvec =0 forLine1inchtestfile.readlines (): Numtestvec+=1line_s1= Line1.strip (). Split ('\ t') lineArr1= [] forJinchRange (21): Linearr1.append (float (line_s1[j)))ifInt (

Full Stack Engineer Development Manual (author: Shangpeng) Python Data Mining Series tutorials GBDT's algorithm reference: https://blog.csdn.net/luanpeng825485697/article/details/79766455 Gradient boosting is a boosting method, and its main idea is that each time a model is established, the gradient descent direction of the model loss function is established. Loss function is the performance of evaluation model (generally fit degree + regular term), t

Reference: http://my.oschina.net/u/175377/blog/84420First: Use Sklearn to import very simple famous flower data--Anderson Iris Floral Data set.We have some measurements of the size of 150 irises: sepals length, width, petal length and width. There are also their sub-genus: The iris setosa, the Iris versicolor, and the Iris virginica Virginia. The data is stored in the. Data entry and is an array (N_samples, n_features). The type of each observation object is stored in the. Target property of the