path to predictive analytics and machine learning amazon
path to predictive analytics and machine learning amazon
Learn about path to predictive analytics and machine learning amazon, we have the largest and most updated path to predictive analytics and machine learning amazon information on alibabacloud.com
parsing text datasets and building contact lens type decision trees are as follows:#------------------------Example: Using decision trees to predict contact lens type----------------def predictlensestype (filename): #打开文本数据 fr= Open (filename) #将文本数据的每一个数据行按照tab键分割 and in turn lenses lenses=[inst.strip (). Split (' \ t ') for Inst in Fr.readlines ()] #创建并存入特征标签列表 lenseslabels=[' age ', ' prescript ', ' astigmatic ', ' tearrate '] # Create a decision tree lensestree=creat
), + Ss_y.inverse_transform (dis_knr_y_predict))) the Print("the average absolute error of the distance weighted K-nearest neighbor regression is:", Mean_absolute_error (Ss_y.inverse_transform (y_test), - Ss_y.inverse_transform (dis_knr_y_predict))) $ the " " the the default evaluation value for the average K-nearest neighbor regression is: 0.6903454564606561 the the r_squared value of the average K-nearest neighbor regression is: 0.6903454564606561 the Mean square error of average K nearest ne
(Ss_y.inverse_transform (y_test), Ss_y.inverse_transform (lr_y_predict)) $ Print("the mean square error of the linear is:", Lr_mse) -Lr_mae =Mean_absolute_error (Ss_y.inverse_transform (y_test), Ss_y.inverse_transform (lr_y_predict)) - Print("the average absolute error of the linear is:", Lr_mae) - A #evaluation of the SGD model +Sgdr_score =Sgdr.score (x_test, y_test) the Print("the default evaluation value for SGD is:", Sgdr_score) -sgdr_r_squared =R2_score (y_test, sgdr_y_predict) $ Print("
regression tree is:", Dtr.score (X_test, y_test)) - Print("the r_squared values for the flat regression tree are:", R2_score (Y_test, dtr_y_predict)) - Print("the mean square error of the regression tree is:", Mean_squared_error (Ss_y.inverse_transform (y_test), - Ss_y.inverse_transform (dtr_y_predict))) A Print("the average absolute error of the regression tree is:", Mean_absolute_error (Ss_y.inverse_transform (y_test), + Ss_y.inverse_transform (dtr_y_predict))) the - " " $ the default evalua
-za-z]"," ", Sent.lower (). Strip ()). Split () in sentences.append (temp) - to returnsentences + - #The sentences in the long news are stripped out for training . thesentences = [] * forIinchx: $Sentence_list =news_to_sentences (i)Panax NotoginsengSentences + =sentence_list - the + #Configure the dimension of the word vector ANum_features = 300 the #the frequency of the words that are to be considered +Min_word_count = 20 - #number of CPU cores used in parallel computing $Num_workers =
Python3 Learning using the APIUsing the data set on the Internet, I downloaded him to a localcan download datasets in my git: https://github.com/linyi0604/MachineLearningCode:1 ImportNumPy as NP2 ImportPandas as PD3 fromSklearn.clusterImportKmeans4 fromSklearnImportMetrics5 6 " "7 K-Mean-value algorithm:8 1 randomly selected K samples as the center of the K category9 2 from the K sample, select the nearest sample to be the same category as yourself,
Python3 Learning using the APIA sample of a data structure of a dictionary type, extracting features and converting them into vector formSOURCE Git:https://github.com/linyi0604/machinelearningCode:1 fromSklearn.feature_extractionImportDictvectorizer2 3 " "4 dictionary feature Extractor:5 pumping and vectorization of dictionary data Structures6 category type features vectorization with 0 12 values using prototype feature names7 numeric type features r
The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion;
products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the
content of the page makes you feel confusing, please write us an email, we will handle the problem
within 5 days after receiving your email.
If you find any instances of plagiarism from the community, please send an email to:
info-contact@alibabacloud.com
and provide relevant evidence. A staff member will contact you within 5 working days.