fitbsues recall

") #计算Nagelkerke拟合优度R2 =r2/(1-exp (-pre$null.deviance)/N) Cat ("Nagelkerke r2=", R2, "\ n") #Nagelkerke r2= 0.7379711 #模型其他指标 #residuals (PRE) #残差 #coefficients (PRE) #系数 #anova (pre) #方差　　4. Accuracy and precisionTRUE_VALUE=AUS_TEST[,15]PREDICT_VALUE=AUS_TEST[,16] #计算模型精度error =predict_value-true_value# to determine the proportion of the correct number in the total Accuracy= (Nrow (aus_test)-sum (ABS (Error)))/nrow (aus_test) #混淆矩阵中的量 (The confusion matrix is explained on the

calculated as the predictive accuracy.①: Rating prediction: General mean-square error (RMSE) and mean absolute error (MAE) calculationRMSE: Recommended system->USERCF Algorithm _ Recommendation System "> MAE:Recommended system->USERCF Algorithm _ Recommendation System "> Import Math def RMSE (Records): Return math.sqrt (sum ([(RUI-PUI) * (RUI-PUI) to U,i,rui,pui in Records])/float ( Len (Records)) def MAE (Records): Return sum ([Math.fabs (RUI-PUI) to U,i,rui,pui in Records])/float (L

machine learning model is really "good"?In this article, we'll look at some common scenarios where seemingly good machine learning models still make mistakes, and discuss how to evaluate these model problems with metrics such as bias (bias) vs variance (variance), precision (precision) vs recall (recall). and propose some solutions for you to use when you encounter such situations.High deviation or high va

: 0.890909090909pos recall: 0.98neg precision: 0.977777777778neg recall: 0.88Most Informative Features magnificent = True pos : neg = 15.0 : 1.0 outstanding = True pos : neg = 13.6 : 1.0 insulting = True neg : pos = 13.0 : 1.0 vulnerable = True pos : neg = 12.3 : 1.0

sixth week. Design of learning curve and machine learning system Learning Curve and machine learning System Design Key Words Learning curve, deviation variance diagnosis method, error analysis, numerical evaluation of machine learning system, big Data principle Overview This week's content is divided into two: First talk. Advice for applying machine learning, the main content is about the deviation, variance and the learning curve as the representative of the diagnostic method, in order to impro

subpoena, search other time is slow, such as search cake recipes on the site of dozens of Or hundreds of cake recipes . When you configure SOLR, you should combine tradeoffs against other factors such as timeliness and ease of use. Two important concepts of relevance: Precision (precision) : Returns the result, The percentage of the document's relevance. Recall : is the percentage of results that are return

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data 11.1 what to do firstIn the next video, I'll talk about the design of the machine learning system. These videos will talk about the major problems you will encounter when designing a complex machine learning system. At the same time we'll try to give some advice on how to build a sophisticated mach

: The number of matched items is not found; Fp: False positive. The matching result is incorrect; TN: Non-matched logarithm correctly rejected; So we can get the matrix on the left and define the accuracy.(Precision)And recall rate(Recall ). Precision: Correct prediction of positive samples/All predicted positive samples; Recall: Correct prediction of po

Skewed classesSkewed classes: The number of a species is much higher (or less than) another class, that is, two extreme cases.Predicting the classification model of cancer, if there is only a 1% classification error on test set, at first glance is a good result, actually if we predict all Y to be 0 (that is, none is cancer), the classification error is 0.5% (because the ratio of cancer is 0.5%). The error is reduced, is this an improvement to the algorithm? Obviously not. Because the latter meth

and movie_target.npy to save time. 3, Code and analysis The code for the logical regression is as follows: #-*-Coding:utf-8-*-from matplotlib import pyplot import scipy as SP import numpy as NP from matplotlib import Pylab F Rom sklearn.datasets import load_files from sklearn.cross_validation import train_test_split from sklearn.feature_ Extraction.text Import Countvectorizer from Sklearn.feature_extraction.text import Tfidfvectorizer from Sklearn.naive_ Bayes import MULTINOMIALNB from sklear

Game on-line for a long time, some players slowly lost, in order to let just lost players back again so do a recall function! If a Level 200 player is not online for 10 days and a successful recall, it will give the recall player a generous bonus! Q: How do I recall this lost player? A: The

(intersection over Union,iou) and the threshold (e.g. 0.5), and the accuracy of the target recognition by the comparison between the confidence score and the threshold value. The above two steps comprehensively determine whether the target detection is correct, finally the multi-category target detection problem is converted to "a certain kind of object detection correct, detection error" of the two classification problem, so that the confusion matrix can be constructed, using a series of indic

]: rate each candidate window with multiple random SEED hyper-pixel maps. The scoring strategy is similar to Objectness's superpixel straddling (no additional information added). The authors show that using multiple hyper-pixel mappings (Superpixel maps) can significantly improve recall rates.2.3 Other proposal methods (alternative proposal methods)?shapesharing [47]: is a non-parametric data-driven method, by matching the edge to transform the target

the saved Movie_data.npy and Movie_target.npy directly to save time.3. Code and AnalysisThe code for logistic regression is as follows:[Python]View PlainCopy #-*-Coding:utf-8-*- From matplotlib import Pyplot Import scipy as SP Import NumPy as NP From matplotlib import Pylab From sklearn.datasets import load_files From sklearn.cross_validation import train_test_split From Sklearn.feature_extraction.text import Countvectorizer From Sklearn.feature_extraction.text import Tfidfv

For the introduction of machine learning, we need some basic concepts:Definition of machine learningM.mitchell the definition in machine learning is:For a certain type of task T and performance Metric p, if a computer program is self-perfecting with experience E in the performance of P measured on T, then we call this computer program to learn from experience E.Algorithm classificationTwo pictures are a good summary of the (machine Learning) algorithm classification:Evaluation indicator Classifi

point of interest or the user's real-time intentions. And we recommend the scene will be with the user's interests, location, environment, time and other changes. The recommendation system mainly faces the following challenges: diversity of Business forms: In addition to the recommended merchant, we also based on different scenarios, real-time judgment, so as to introduce different forms of business, such as the group, hotels, attractions, overlord meal. user consumption scene dive

IntroductionUnlike most of the recommended systems, the scene of the US reviews is due to the diversity of its business, making it difficult to accurately capture the user's point of interest or the User's real-time Intentions. And we recommend the scene will be with the User's interests, location, environment, time and other Changes. The recommendation system mainly faces the following challenges: diversity of Business forms: in addition to the recommended merchant, we also based on d

The code basically comes from the light You can also add a normalization of the user similarity, and the effect will be better.The data set is 100,000 data of movielens.Links: Moivelens#Coding:utf-8ImportRandom,math fromoperatorImportItemgetterclassUserbasedcf:def __init__(self,traindatafile=none,testdatafile=none,splitor='\ t'): iftraindatafile!=None:self.train=self.loaddata (traindatafile, Splitor)iftestdatafile!=None:self.test=self.loaddata (testdatafile, splitor) Self.simimatrix={}

Take value Wubi Gaga Microsoft Pinyin 3.0 Sogou Pinyin(Applicable to mainstream Chinese input method) Description Nocontrol Press Ctrl+space once for the first call to correctly use Chinese and Western punctuation or full half-width characters to inherit the last setting After the default is the English input state is paged out after default to Western punctuation in English input is half-width character After the default is the English input state is page

)) "accuracy and recall rate " Precision, recall, thresholds = Precision_recall_curve (Y_train, Clf.predict (X_train)) Answer = Clf.predict_proba (x) [:,1] Print (Classification_report (y, answer, target_names = [' thin ', ' fat ')]) The output looks similar to the following: [0.2488562 0.7511438] Array ([[1.6, 60.], [1.7, 60. ], [1.9, 80.], [1.5, 50.], nbs P [1.6, 40.], [1.