sklearn auc

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to evaluate the information retrieval system, and the average accuracy map is defined as follows: (of which p,r is the accuracy and recall rate respectively)ROC and AUCThe ROC and AUC are indicators of the evaluation classifier, and the ABCD of the first figure above is still used, just a slight transformation is needed.Returning to Roc, the ROC's full name is called receiver Operating characteristic.ROC Focus on two indicatorsTrue Positive Rate (TPR

fusion of multiple models. "Everything is a ensemble", "Heads top Zhuge Liang", integrated the advantages of different algorithms, to avoid weaknesses, integration of a cow-breaking super model. Many internet companies in a single model after the bottleneck, most will adopt model fusion, more popular is GBDT+LR. Fundamentally, the fusion of multiple models is essentially the output of one model as the input of another model, and the first model acts as the role of feature conversion. Effect Ass

(oversampling, equivalent to interpolation), lower sampling (downsampling, equivalent to compression), two-stage training (two-phase training), and Threshold (threholding), The threshold value can compensate for the Transcendental category probability. Because global accuracy is difficult to determine in unbalanced data, our main evaluation indicator is the area below the ROC curve (Roc AUC). From our experiment we can draw the following conclusions:

) Call The kNN algorithm in scikit-learn. # Call The knn algorithm package of scikit from sklearn. neighbors import into def knnClassify (trainData, trainLabel, testData): knnClf = encrypt () # default: k = 5, defined by yourself: KNeighborsClassifier (n_neighbors = 10) knnClf. fit (trainData, ravel (trainLabel) testLabel = knnClf. predict (testData) saveResult(testLabel,'sklearn_knn_Result.csv ') return testLabel The kNN algorithm package can set its

Sesame HTTP: Remembering the pitfalls of scikit-learn Bayesian text classification, scikit-learn Bayes Basic steps: 1. Training material classification: I am referring to the official directory structure: Put the corresponding text in each directory, a txt file, and a corresponding article: like the following: Please note that the proportion of all materials should be kept in the same proportion (adjusted according to the training results as appropriate, the ratio is too large, and it is easy

. Error ()) }}var pystr = python. Pystring_fromstringvar gostr = python. Pystring_as_stringfuncmain () {//import Helloinsertbeforesyspath ("/users/vonng/anaconda2/lib/python2.7/ Site-packages ") Hello: = Importmodule ("/users/vonng/dev/go/src/gitlab.alibaba-inc.com/cplus "," Hello ") FMT. Printf ("[MODULE] repr (hello) =%s\n", Gostr (hello. Repr ())) //print (HELLO.A) A: = hello. Getattrstring ("a") FMT. Printf ("[VARS] a =% #v \ n", Python. Pyint_aslong (a)) //print (HELLO.B) B: =

The person who has refined himselfYou are welcome to visit my Pinterest and my blog.This blog all content to study, research and sharing mainly, if need to reprint, please contact me, marked the author and source, and is non-commercial use, thank you!Abstract : This paper mainly describes the semi-supervised algorithm to do text classification (two classification), the main reference is an example of Sklearn-semi-supervised algorithm to do digital rec

generate a high-weight TF-IDF. Therefore, TF-IDF tends to filter out common words and retain important words. For a detailed introduction and examples of TF-IDF, interested students can read this blog. The following describes how to use the TF-IDF in Python. Second, Python computing TF-IDF in Python, scikit-learn package under the calculation of TF-IDF api, the effect is also very good. First install Scikit-clearn. For installation of different systems, see http://scikit-learn.org/stable/instal

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Research machine learning will study the classification algorithm, when the model of a classification algorithm is established, the quality of the model needs to be quantified, the most important is the classifier evaluation index. The following is mainly about the indicators of the classifier. (Here, the main is to introduce the two categories of classifier evaluation indicators)Below we can look at the analysis of two types of results:1, accuracy (correct rate)It represents the correct proport

, you can set shrinkage to a smaller value and the number of trees to a larger value.Sample_rate: sample sampling rate. to construct a model with different tendencies, we need to use a subset of the samples for training. Excessive samples may cause more overfitting and local extremely small problems. The sampling ratio is generally 50%-70%.Variable_sample_rate: feature sampling rate, which refers to learning from the features selected from all the features of the sample without using all the fea

is better to embed the RND random number in the-D option, which is more confusing ). When port 80 is detected, the target host replies the SYN/ACK packet back to us (of course, we cannot receive the SYN/ACK packet from other spoofed IP addresses ), it proves that port 80 is open. 3.2 AUC Script Engine The NMAP scripting engine is one of the most powerful and flexible NMAP functions. It allows you to write your own scripts to perform automated operat

different tendencies, we need to use a subset of the samples for training. Excessive samples may cause more overfitting and local extremely small problems. The sampling ratio is generally 50%-70%.Variable_sample_rate: feature sampling rate, which refers to learning from the features selected from all the features of the sample without using all the features. When one or two features of the trained model are found to be very strong and important, and other features are basically unavailable, you

=[(1+B2) *p*r]/(B2*p+r), the more commonly used is F1.In the information retrieval, the accuracy rate and the recall rate are mutual influence, although both are high is an ideal situation, but in practice, the accuracy is often high, the recall rate is low, or the recall rate is low, but the accuracy rate is high. So in practice often need to make a choice according to the specific situation, for example, the general search situation is to ensure that the recall rate to improve the accuracy rat