際際滷

際際滷Share a Scribd company logo

AMS description

E
EUNJONG KIM

H2O is an open-source machine learning platform that allows users to easily build and deploy machine learning models. It supports many common machine learning algorithms and deep learning. The Automatic Model Selector (AMS) in H2O aims to simplify the model selection process by automatically finding the optimal model with minimal input from the user. AMS requires only three inputs - the data file path, target variable, and whether the target is numeric or categorical. It then automatically trains and compares various models to select the best one. AMS supports many common machine learning algorithms in H2O and runs quickly using multi-threading. It can save models for reuse and deployment, making it useful for both model development and productionization.

1 of 11
Download to read offline
Automatic Model Selection 蟾譬
Contents 01. H2O Framework 02. AMS 03.
H2O Framework? H2O.ai is focused on bringing AI to businesses through software. Its flagship product is H2O, the leading open source platform that makes it easy for financial services, insurance companies, and healthcare companies to deploy AI and deep learning to solve complex problems. More than 9,000 organizations and 80,000+ data scientists depend on H2O for critical applications like predictive maintenance and operational intelligence. The company which was recently named to the CB Insights AI 100 is used by 169 Fortune 500 enterprises, including 8 of the worlds 10 largest banks, 7 of the 10 largest insurance companies, and 4 of the top 10 healthcare companies. Notable customers include Capital One, Progressive Insurance, Transamerica, Comcast, Nielsen Catalina Solutions, Macys, Walgreens, and Kaiser Permanente. Using in-memory compression, H2O handles billions of data rows in-memory, even with a small cluster. To make it easier for non-engineers to create complete analytic workflows, H2Os platform includes interfaces for R, Python, Scala, Java, JSON, and CoffeeScript/JavaScript, as well as a built-in web interface, Flow. H2O is designed to run in standalone mode, on Hadoop, or within a Spark Cluster, and typically deploys within minutes. H2O includes many common machine learning algorithms, such as generalized linear modeling (linear regression, logistic regression, etc.), Na即脹ve Bayes, principal components analysis, k-means clustering, and word2vec. H2O implements bestin-class algorithms at scale, such as distributed random forest, gradient boosting, and deep learning. H2O also includes a Stacked Ensembles method, which finds the optimal combination of a collection of prediction algorithms using a process known as stacking. With H2O, customers can build thousands of models and compare the results to get the best predictions. What is H2O?
H2O Framework? What is H2O? Java 蠍磯 Multi Thread 讌 / In-Memory Computing (螳 觜襯企) 蠍 覓癌讌 螳ｸ R / Python 語 讌 (蠏碁 Python ) Spark 讌 (Sparkling Water) 豕 Machine Learning 螻襴讀 讌 - Light-GBM,DNN,GLM,DistributedRandomForest,Extremely-RandomizedTrees, Deep Learning 螳 WOW
Automatic Model Selector AMS 螳 Hyper Parameter 螻 覈 觜蟲螳 覓 覯蟇磯´. - AUC, log-loss, 一伎襷 GINI螻 觜蟲 Input 螳 豕 蟆 ロ螻 朱 豕覈語 蟆郁骸螳朱 觸企 企蟾?
Automatic Model Selector AMS 螳 Hyper Parameter 螻 覈 觜蟲螳 覓 覯蟇磯´. - AUC, log-loss, 一伎襷 GINI螻 觜蟲 Input 螳 豕 蟆 ロ螻 朱 豕覈語 蟆郁骸螳朱 觸企 企蟾? 豢覦 (蠏谿朱蟾 貉危壱 蟆 れ)
Automatic Model Selector AMS 轟 H2O Framework 伎 豕 Machine Learning 螻襴讀 讌 - Light-GBM,DNN,GLM,DistributedRandomForest,Extremely-RandomizedTrees, Input螳 3螳 : 蟆暑, Target 覲, Target 覲螳 豺(Regression)語 覯譯狩(Classification)語 Type 覈 覲 MOJO朱 所 リ, 覿り鍵 暑. () Multi Thread 讌朱 豌 觜襯企. Python 蠍磯 渚.
Automatic Model Selector Save & Run Result AMS 蟲譟磯
Automatic Model Selector AMS 蟆郁骸
Automatic Model Selector
Thank you https://github.com/yoonslab

More Related Content

AMS description

  • 3. H2O Framework? H2O.ai is focused on bringing AI to businesses through software. Its flagship product is H2O, the leading open source platform that makes it easy for financial services, insurance companies, and healthcare companies to deploy AI and deep learning to solve complex problems. More than 9,000 organizations and 80,000+ data scientists depend on H2O for critical applications like predictive maintenance and operational intelligence. The company which was recently named to the CB Insights AI 100 is used by 169 Fortune 500 enterprises, including 8 of the worlds 10 largest banks, 7 of the 10 largest insurance companies, and 4 of the top 10 healthcare companies. Notable customers include Capital One, Progressive Insurance, Transamerica, Comcast, Nielsen Catalina Solutions, Macys, Walgreens, and Kaiser Permanente. Using in-memory compression, H2O handles billions of data rows in-memory, even with a small cluster. To make it easier for non-engineers to create complete analytic workflows, H2Os platform includes interfaces for R, Python, Scala, Java, JSON, and CoffeeScript/JavaScript, as well as a built-in web interface, Flow. H2O is designed to run in standalone mode, on Hadoop, or within a Spark Cluster, and typically deploys within minutes. H2O includes many common machine learning algorithms, such as generalized linear modeling (linear regression, logistic regression, etc.), Na即脹ve Bayes, principal components analysis, k-means clustering, and word2vec. H2O implements bestin-class algorithms at scale, such as distributed random forest, gradient boosting, and deep learning. H2O also includes a Stacked Ensembles method, which finds the optimal combination of a collection of prediction algorithms using a process known as stacking. With H2O, customers can build thousands of models and compare the results to get the best predictions. What is H2O?
  • 4. H2O Framework? What is H2O? Java 蠍磯 Multi Thread 讌 / In-Memory Computing (螳 觜襯企) 蠍 覓癌讌 螳ｸ R / Python 語 讌 (蠏碁 Python ) Spark 讌 (Sparkling Water) 豕 Machine Learning 螻襴讀 讌 - Light-GBM,DNN,GLM,DistributedRandomForest,Extremely-RandomizedTrees, Deep Learning 螳 WOW
  • 5. Automatic Model Selector AMS 螳 Hyper Parameter 螻 覈 觜蟲螳 覓 覯蟇磯´. - AUC, log-loss, 一伎襷 GINI螻 觜蟲 Input 螳 豕 蟆 ロ螻 朱 豕覈語 蟆郁骸螳朱 觸企 企蟾?
  • 6. Automatic Model Selector AMS 螳 Hyper Parameter 螻 覈 觜蟲螳 覓 覯蟇磯´. - AUC, log-loss, 一伎襷 GINI螻 觜蟲 Input 螳 豕 蟆 ロ螻 朱 豕覈語 蟆郁骸螳朱 觸企 企蟾? 豢覦 (蠏谿朱蟾 貉危壱 蟆 れ)
  • 7. Automatic Model Selector AMS 轟 H2O Framework 伎 豕 Machine Learning 螻襴讀 讌 - Light-GBM,DNN,GLM,DistributedRandomForest,Extremely-RandomizedTrees, Input螳 3螳 : 蟆暑, Target 覲, Target 覲螳 豺(Regression)語 覯譯狩(Classification)語 Type 覈 覲 MOJO朱 所 リ, 覿り鍵 暑. () Multi Thread 讌朱 豌 觜襯企. Python 蠍磯 渚.
  • 8. Automatic Model Selector Save & Run Result AMS 蟲譟磯