idatamining.net

iDataMining.net - 爱数据挖掘 飞翔

Modern Methods for Sentiment Analysis. Modern Methods for Sentiment Analysis. Gbdt auc .5f. 基于原有特征的LR AUC .5f. 基于GBDT特征编码后的LR AUC .5f. 基于组合特征的LR AUC .5f. Datasample libsvm data.txt. Xgboost test auc .5f. Transform features genenrated by xgboost. Write xgboost learned features to file . 基于原有特征的LR AUC .5f. 基于Xgboost特征编码后的LR AUC .5f. 基于组合特征的LR AUC .5f. Datasample libsvm data.txt. Datasample libsvm data.txt. Loss functions for classification. 风控 如何构建 FinTech 科学反欺诈体系. 修改保存后下.
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The web page idatamining.net presently has an average traffic classification of zero (the lower the more traffic). There is one contacts and locations for idatamining.net to help you reach them. The web page idatamining.net has been online for five hundred and thirty weeks, twenty-eight days, thirteen hours, and nineteen minutes.
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May 2014

IDATAMINING.NET TRAFFIC

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IDATAMINING.NET HISTORY

The web page idatamining.net was first recorded on May 09, 2014. This domain will expire on the date of May 09, 2015. It is now five hundred and thirty weeks, twenty-eight days, thirteen hours, and nineteen minutes young.
REGISTERED
May
2014
EXPIRED
May
2015

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10
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WHAT DOES IDATAMINING.NET LOOK LIKE?

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CONTACTS

None

Feixiang Gao

HIT Campus of University Town of Shenzhen

Shenzhen, Shenzhen, 518055

China

86.15019470660
gaofeixiangcs@gmail.com

IDATAMINING.NET SERVER

Our crawlers diagnosed that a single root page on idatamining.net took two thousand one hundred and fifty milliseconds to come up. I could not observe a SSL certificate, so in conclusion I consider idatamining.net not secure.
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2.15 sec
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NOT SECURE
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23.88.246.65

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We caught that this domain is utilizing the Apache/2.2.22 (Ubuntu) operating system.

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iDataMining.net - 爱数据挖掘 飞翔

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Modern Methods for Sentiment Analysis. Modern Methods for Sentiment Analysis. Gbdt auc .5f. 基于原有特征的LR AUC .5f. 基于GBDT特征编码后的LR AUC .5f. 基于组合特征的LR AUC .5f. Datasample libsvm data.txt. Xgboost test auc .5f. Transform features genenrated by xgboost. Write xgboost learned features to file . 基于原有特征的LR AUC .5f. 基于Xgboost特征编码后的LR AUC .5f. 基于组合特征的LR AUC .5f. Datasample libsvm data.txt. Datasample libsvm data.txt. Loss functions for classification. 风控 如何构建 FinTech 科学反欺诈体系. 修改保存后下.

PARSED CONTENT

The web page has the following in the web page, "Modern Methods for Sentiment Analysis." I saw that the web page also stated " Transform features genenrated by xgboost." They also said " Write xgboost learned features to file ."

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