scoring | 易学教程

how to solve errors related to scores and proceed to next round in android general knowledge quiz?

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问题 i am working on android quiz. when i run it on emulator first question appear and i answer it. its stopped working. and then restart from start. i dont know where's problem in my code. questionactivity.class import java.util.List; import android.app.Activity; import android.content.Intent; import android.os.Bundle; import android.view.KeyEvent; import android.view.View; import android.view.View.OnClickListener; import android.widget.Button; import android.widget.TextView; public class

Elasticsearch - similary for countries

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问题 I have a document, which contains many fields, one of them is country . There are many documents with the same country . When I do match query , or fuzzy search against country , and query for Belgium for example, it returns list of documents, which matched Belgium country, but they all have different score. I believe it's because of tdidf similarity and presence of belgium term in other fields of documents, etc. I'd like it return the same score in this case. What similarity should I use?

Implementing custom solr similarity

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问题 Currently I need to implement custom solr similarity. So I found out that I need to override the DefaultSimilarity class in order to do this. Still I can't figure out how exactly it should be done and where to get source code which can be used for this purpose. Any help would be appreciated! 回答1: For anyone who will need an answer: What I needed to do was to create a package project in eclipse, download lucene-core jar and add it to the project. After that I imported the needed library and

图像分割-Mask Scoring R-CNN

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转载： https://zhuanlan.zhihu.com/p/58291808 论文链接： https:// arxiv.org/abs/1903.0024 1 代码链接： https://github.com/zjhuang22/maskscoring_rcnn 今天介绍一篇CVPR2019的论文，来自华科和地平线，这篇论文从实例分割中mask 的分割质量角度出发，提出过去的经典分割框架存在的一个缺陷：用Bbox bounding box的classification confidence作为mask score，导致mask score和mask quality不配准。因此文章基于Mask R-CNN提出一个新的框架Mask Scoring R-CNN，能自动学习出mask quality，试图解决不配准的问题。在实例分割（instance segmentation）中，比如Mask R-CNN，mask 分支的分割质量（quality）来源于检测分支的classification confidence。Mask R-CNN其实Faster R-CNN系列的延伸，其在Faster R-CNN的基础上添加一个新的分支用来预测object mask，该分支以检测分支的输出作为输入，mask的质量一定程度上依赖于检测分支。这种简单粗暴的做法取得了SOTA的性能

AUC-base Features Importance using Random Forest

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问题 I'm trying to predict a binary variable with both random forests and logistic regression. I've got heavily unbalanced classes (approx 1.5% of Y=1). The default feature importance techniques in random forests are based on classification accuracy (error rate) - which has been shown to be a bad measure for unbalanced classes (see here and here). The two standard VIMs for feature selection with RF are the Gini VIM and the permutation VIM. Roughly speaking the Gini VIM of a predictor of interest

elasticsearch disable term frequency scoring

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问题 I want to change the scoring system in elasticsearch to get rid of counting multiple appearances of a term. For example, I want: "texas texas texas" and "texas" to come out as the same score. I had found this mapping that elasticsearch said would disable term frequency counting but my searches do not come out as the same score: "mappings":{ "business": { "properties" : { "name" : { "type" : "string", "index_options" : "docs", "norms" : { "enabled": false}} } } } } Any help will be appreciated

Can I insert a Document into Lucene without generating a TokenStream?

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问题 Is there a way to add a document to the index by supplying terms and term frequencies directly, rather than via Analysis and/or TokenStream? I ask because I want to model some data where I know the term frequencies, but there is no underlying text document to be analyzed. I could create one by repeating the same term many times (I don't care about positions or highlighting in this case, either, just scoring), but that seems a bit perverse (and probably slower than just supplying the counts

Grid search for hyperparameter evaluation of clustering in scikit-learn

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问题 I'm clustering a sample of about 100 records (unlabelled) and trying to use grid_search to evaluate the clustering algorithm with various hyperparameters. I'm scoring using silhouette_score which works fine. My problem here is that I don't need to use the cross-validation aspect of the GridSearchCV / RandomizedSearchCV , but I can't find a simple GridSearch / RandomizedSearch . I can write my own but the ParameterSampler and ParameterGrid objects are very useful. My next step will be to

ElasticSearch: Partial/Exact Scoring with edge_ngram & fuzziness

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In ElasticSearch I am trying to get correct scoring using edge_ngram with fuzziness. I would like exact matches to have the highest score and sub matches have lesser scores. Below is my setup and scoring results. settings: { number_of_shards: 1, analysis: { filter: { ngram_filter: { type: 'edge_ngram', min_gram: 2, max_gram: 20 } }, analyzer: { ngram_analyzer: { type: 'custom', tokenizer: 'standard', filter: [ 'lowercase', 'ngram_filter' ] } } } }, mappings: [{ name: 'voter', _all: { 'type': 'string', 'index_analyzer': 'ngram_analyzer', 'search_analyzer': 'standard' }, properties: { last: {

Can I insert a Document into Lucene without generating a TokenStream?

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Is there a way to add a document to the index by supplying terms and term frequencies directly, rather than via Analysis and/or TokenStream? I ask because I want to model some data where I know the term frequencies, but there is no underlying text document to be analyzed. I could create one by repeating the same term many times (I don't care about positions or highlighting in this case, either, just scoring), but that seems a bit perverse (and probably slower than just supplying the counts directly). (also asked on the mailing list) At any rate, you don't need to pass everything through an