sklearn auc

SK-Learn family, sk-learn familySK-Learn API family Recently, SK-Learn has been widely used and will be used frequently in the future. I have sorted out all Sk-Learn content, sorted out my ideas, and made it available for future reference. (You can right-click an image to open it in a separate window or save it to a local device)Basic public base sklearn. cluster sklearn. datasets Loaders Samples generator

= Np.random.randint (2, size=5)#binary TargetDtrain = XGB. Dmatrix (data, label=label) Dtest=dtrain param= {'bst:max_depth': 2,'Bst:eta': 1,'Silent': 1,'Objective':'binary:logistic'} param['Nthread'] = 4param['Eval_metric'] ='AUC'evallist= [(Dtest,'Eval'), (Dtrain,'Train')] Num_round= 10BST=xgb.train (param, Dtrain, Num_round, evallist) Bst.dump_model ('Dump.raw.txt')Output:[0] eval-auc:0.5 train-auc

supervised Best-ks,chimerge (Card sub-box), non-supervised including equal frequency, equidistant, clustering. According to the data characteristics, different bins are used for different data. The code is as follows:3.1.2 Woe value calculationDefine the woe value and evaluate it.3.1.3 Calculating the IV valueThe full name of IV is information value, which means the value of information, or the amount of data. Figure 13 is the IV value for each variable. We define a feature with an IV value bel

mathematical expressions are: Among them, Yi refers to the real class I samples belong to 0 or 1,PI indicates that the first sample belongs to the probability of category 1, so that the two parts of the formula will only choose one for each sample, because there is a certain 0, when the prediction and the actual category exactly match, then two parts are 0, which assumes 0log0=0.AUC (area under Curve)ClickThrough Rate for CTR (click through rate) onl

AUC optimized models. In *jmlr:workshop and Conference proceedings*, Vol, pp. 109-127. A domain Unknown to me: It's the best-of-the-learn about-to-work with a different kind of data. The need to preprocess and extract the features from raw data to build the dataset: It gives the chance to use your intuition and imagination. This challenge looked very interesting to me because all the conditions were met.Let's Get Technicalwhat prepr

smote-supersampling Rare events in R: Super-sampling rare events with RIn this example, the following three packages will be used{DMWR}-Functions and data for the book ' Data Mining with R ' and SMOTE algorithm:smote algorithm{Caret}-modeling wrapper, functions, commands: Model encapsulation, functions, commands{PROC}-area under the Curve (AUC) functions: Under-curve (ACU) functionThe smote algorithm is designed to solve the problem of imbalance class

KNN (K Nearest Neighbor) for Machine Learning Based on scikit-learn package-complete example, scikit-learnknn KNN (K Nearest Neighbor) for Machine Learning Based on scikit-learn package) Scikit-learn (sklearn) is currently the most popular and powerful Python library for machine learning. It supports a wide range Class, clustering, and regression analysis methods, such as support vector machine, random forest, and DBSCAN. He has been welcomed by many

. In data mining, we often use decision trees for data classification and prediction. Helloworld of decision tree In this section, we use decision trees to classify and predict iris data sets. Here we will use graphviz of the tree under sklearn to help export the decision tree and store it in pdf format. The Code is as follows: # The helloworld of the decision tree uses the decision tree to classify the iris dataset from

Since the development of wireless communication technology, various wireless standard wireless systems have brought many security risks. So how can we ensure the security of wireless access? Next, we will introduce in detail various wireless access security mechanisms, principles, and processes. Wireless Access Security of the 3GPP system Wireless Access Security for GSM/GPRS/EDGE SystemsIn the GSM/GPRS/EDGE system, the user's SIM card shares a security key Ki128bit with the HLR/

replace spaces with underscores to make the final file name safer. If an error occurs during file upload, the class throws an exception object that provides information about the error code and description of the error message. Code beaded: http://www.codepearl.com/files/194.html Source code and demo:Source code source demonstration Source --> $velocityCount--> --> upload_dir("directory name", "create dir if it does not exist, false by default or true");//$

Ec (2); lt ;? Php $ action $ _ GET [action]; require_once (auc. main. class. inc. php); $ aucnewauc (); if ($ actionuploadfile) { nbsp; $ aucnewauc (); nbsp; $ result $ auc- gt; upload ( script ec (2); script $ Action = $ _ GET ['action'];Require_once ('auc. main. class. inc. php '); $ Auc = new

/*PR curve and AOC curve*/Val METRICSNB=Seq (model_nb). Map{model=Val socreandlabels=Datanb.map { point=(model.predict (point.features), point.label)} Val Metrics=Newbinaryclassificationmetrics (socreandlabels) (MODEL.GETCLASS.GETSIMPLENAME,METRICS.AREAUNDERPR (), Metrics.areaunderroc ())}metricsnb.foreach{ case(m, pr, Roc) = =println (f"$m, area under PR: ${PR * 100.0}%2.4f%%, area under ROC: ${roc * 100.0}%2.4f%%") }/*naivebayesmodel, area under pr:74.0522%, area under roc:60.5138%*/2, Modi

don't know what that means. ]What is the difference between the first order and the Hi Jiezheng? What are the occasions for each?The biggest difference between the two is whether the feature coefficient will be 0, first-order penalty can not only reduce the complexity of the model, but also to complete the feature screening, that is, the coefficient of the partial feature is reduced to 0, the second penalty may reduce the coefficient of some features to a small, but generally will not reduce th

exists. if it does not exist, createOS. makedirs (seg_dir)File_list = OS. listdir (class_path)For file_pathin file_list:Fullname = class_path + file_pathContent = readfile (fullname). strip () # read file contentContent = content. replace ("\ r \ n", ""). strip () # delete line breaks and extra spacesContent_seg = jieba. cut (content)Savefile (seg_dir + file_path, "". join (content_seg ))Print ("Word Segmentation ends ") For the convenience of generating the word vector space model in the futur