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History: Machine Learning Models
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Source of version: 15
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{img src="display1737" link="display1737" width="1000" rel="box[g]" imalign="center" desc="Click to expand" align="center" styleimage="border"} You can create ((Machine Learning)) models from scratch or from templates !! Templates The template is the best approach to begin creating your machine learning model. It allows us to create a machine learning model based on commonly observed problems, for example the MLT. {img type="attId" attId="95"} !! Available Templates Actually Tiki only support one template : !! More Like This (MLT) The MLT template solves the problems associated with suggesting similar content (finds documents that are "like" a given set of documents). This emulates ((Module More Like This)) More info: https://github.com/RubixML/RubixML/issues/75 !!! Transformers and Learners for MoreLikeThis {FANCYTABLE(head="__Transformers and Applied Learners__|__Arguments__" sortable="n")} TextNormalizer | StopWordFilter | WordCountVectorizer| maxVocabulary :1000 , minDocumentFrequency :1 ,maxDocumentFrequency: 500 ,okenizer :default BM25Transformer | alpha :1.2 , beta :0.75 KDNeighbors | k:20, weighted:true, tree : BallTree {FANCYTABLE} {img src="display1748" link="display1748" width="800" rel="box[g]" imalign="center" desc="Click to expand" align="center" styleimage="border"} %%% {HTML()} <style> .thumbcaption { display: none; } #page-data > p:first-of-type, .wikipreview .wikitext > p:first-of-type { font-size: 120%; padding: 2rem; background: #f0f0f0; border-radius: .0rem; } </style> {HTML}
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