scoring

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 subclass BaseSearchCV and implement my own _fit() method, but thought it was worth asking is there a simpler

I am searching "product documents". In other words, my solr documents are product records. I want to get say the top 50 matching products for a query. Then I want to be able to sort the top 50 scoring documents by name or price. I'm not seeing much on how to do this, since sorting by score, then by name or price won't really help, since scores are floats. I wouldn't mind if I could do something like map the scores to ranges (like a score of 8.0-8.99 would go in the 8 bucket score), then sort by range, then by names, but since there is basically no normalization to scoring, this would still

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 is the sum over the forest of the decreases of Gini impurity generated by this predictor whenever it

Python机器学习项目的模板 1.定义问题 a)导入类库 b)导入数据集 from sklearn.datasets import load_files from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import MultinomialNB from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report from sklearn.metrics import accuracy_score from sklearn.model_selection import cross_val_score from sklearn.model_selection

GridSearchCV 简介： 常用参数解读： estimator：所使用的分类器，如estimator=RandomForestClassifier(min_samples_split=100,min_samples_leaf=20,max_depth=8,max_features=‘sqrt‘,random_state=10), 并且传入除需要确定最佳的参数之外的其他参数。每一个分类器都需要一个scoring参数，或者score方法。 param_grid：值为字典或者列表，即需要最优化的参数的取值，param_grid =param_test1，param_test1 = {‘n_estimators‘:range(10,71,10)}。 scoring :准确度评价标准，默认None,这时需要使用score函数；或者如scoring=‘roc_auc‘，根据所选模型不同，评价准则不同。字符串（函数名），或是可调用对象，需要其函数签名形如：scorer(estimator, X, y)；如果是None，则使用estimator的误差估计函数。scoring参数选择如下： 参考地址： http://scikit-learn.org/stable/modules/model_evaluation.html iid:默认True,为True时，默认为各个样本fold概率分布一致

What I am looking for, is plain, clear explanation, of how default scoring mechanism of ElasticSearch (Lucene) really works. I mean, does it use Lucene scoring, or maybe it uses scoring of its own? For example, I want to search for document by, for example, "Name" field. I use .NET NEST client to write my queries. Let's consider this type of query: IQueryResponse<SomeEntity> queryResult = client.Search<SomeEntity>(s => s.From(0) .Size(300) .Explain() .Query(q => q.Match(a => a.OnField(q.Resolve(f => f.Name)).QueryString("ExampleName"))) ); which is translated to such JSON query: { "from": 0,

My game has a collision detection where when my missile hits the enemy the enemy disappears. I want to add a scoring system that adds 1 point every time my missile hits the enemy. I'll post my game code below (I used the HelloWorldLayer.m)** Here is the code: Link - http://pastebin.com/iGP83SCv In the collision section i just want it to add 1 point every time the projectile hits the enemy "sprout" and display the score in label. Example: Score:0000 PS, please explain it as easy as possible. @synthesize a "score" property of type int , and a "scoreLabel" property of type CCLabelTTF . initialize

I have the following records and the scores against it when I search for "iphone" - Record1: FieldName - DisplayName : "Iphone" FieldName - Name : "Iphone" 11.654595 = (MATCH) sum of: 11.654595 = (MATCH) max plus 0.01 times others of: 7.718274 = (MATCH) weight(DisplayName:iphone^10.0 in 915195), product of: 0.6654692 = queryWeight(DisplayName:iphone^10.0), product of: 10.0 = boost 11.598244 = idf(docFreq=484, maxDocs=19431244) 0.0057376726 = queryNorm 11.598244 = (MATCH) fieldWeight(DisplayName:iphone in 915195), product of: 1.0 = tf(termFreq(DisplayName:iphone)=1) 11.598244 = idf(docFreq=484,