{"id":9144685625618,"title":"BigML Update a Topic Distribution Integration","handle":"bigml-update-a-topic-distribution-integration","description":"\u003cbody\u003e```html\n\n\n\n \u003cmeta charset=\"UTF-8\"\u003e\n \u003cmeta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\"\u003e\n \u003ctitle\u003eBigML Update a Topic Distribution API\u003c\/title\u003e\n \u003cstyle\u003e\n body {\n font-family: Arial, sans-serif;\n }\n h1 {\n color: #333;\n }\n p, ul {\n line-height: 1.6;\n }\n \u003c\/style\u003e\n\n\n \u003ch1\u003eBigML Update a Topic Distribution API\u003c\/h1\u003e\n \u003cp\u003eThe BigML Update a Topic Distribution API is a powerful resource that allows users to programmatically manage their topic distributions in a machine learning environment provided by BigML. Topic distributions are part of topic modeling, a technique in machine learning to classify documents or text samples based on their topics. The API is particularly useful in maintaining and enhancing the accuracy of models as new data comes in or when the parameters guiding the learning process need refinement.\u003c\/p\u003e\n \n \u003ch2\u003eCapabilities of the API\u003c\/h2\u003e\n \u003cp\u003eThe API enhances productivity and the predictive accuracy of text corpus management by allowing users to:\u003c\/p\u003e\n \u003cul\u003e\n \u003cli\u003eModify topic distribution properties post-creation; for example, updating the name or description of the distribution.\u003c\/li\u003e\n \u003cli\u003eRefine the output of existing topic distributions by adjusting thresholds or\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/body\u003e","published_at":"2024-03-13T01:10:56-05:00","created_at":"2024-03-13T01:10:57-05:00","vendor":"BigML","type":"Integration","tags":[],"price":0,"price_min":0,"price_max":0,"available":true,"price_varies":false,"compare_at_price":null,"compare_at_price_min":0,"compare_at_price_max":0,"compare_at_price_varies":false,"variants":[{"id":48259830513938,"title":"Default Title","option1":"Default Title","option2":null,"option3":null,"sku":"","requires_shipping":true,"taxable":true,"featured_image":null,"available":true,"name":"BigML Update a Topic Distribution Integration","public_title":null,"options":["Default Title"],"price":0,"weight":0,"compare_at_price":null,"inventory_management":null,"barcode":null,"requires_selling_plan":false,"selling_plan_allocations":[]}],"images":["\/\/consultantsinabox.com\/cdn\/shop\/products\/6fe35b0c07f01f3799363a654ec5f215_1df632b7-fb2a-4f2d-bd47-c383aedce6d9.png?v=1710310257"],"featured_image":"\/\/consultantsinabox.com\/cdn\/shop\/products\/6fe35b0c07f01f3799363a654ec5f215_1df632b7-fb2a-4f2d-bd47-c383aedce6d9.png?v=1710310257","options":["Title"],"media":[{"alt":"BigML Logo","id":37928052621586,"position":1,"preview_image":{"aspect_ratio":2.121,"height":264,"width":560,"src":"\/\/consultantsinabox.com\/cdn\/shop\/products\/6fe35b0c07f01f3799363a654ec5f215_1df632b7-fb2a-4f2d-bd47-c383aedce6d9.png?v=1710310257"},"aspect_ratio":2.121,"height":264,"media_type":"image","src":"\/\/consultantsinabox.com\/cdn\/shop\/products\/6fe35b0c07f01f3799363a654ec5f215_1df632b7-fb2a-4f2d-bd47-c383aedce6d9.png?v=1710310257","width":560}],"requires_selling_plan":false,"selling_plan_groups":[],"content":"\u003cbody\u003e```html\n\n\n\n \u003cmeta charset=\"UTF-8\"\u003e\n \u003cmeta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\"\u003e\n \u003ctitle\u003eBigML Update a Topic Distribution API\u003c\/title\u003e\n \u003cstyle\u003e\n body {\n font-family: Arial, sans-serif;\n }\n h1 {\n color: #333;\n }\n p, ul {\n line-height: 1.6;\n }\n \u003c\/style\u003e\n\n\n \u003ch1\u003eBigML Update a Topic Distribution API\u003c\/h1\u003e\n \u003cp\u003eThe BigML Update a Topic Distribution API is a powerful resource that allows users to programmatically manage their topic distributions in a machine learning environment provided by BigML. Topic distributions are part of topic modeling, a technique in machine learning to classify documents or text samples based on their topics. The API is particularly useful in maintaining and enhancing the accuracy of models as new data comes in or when the parameters guiding the learning process need refinement.\u003c\/p\u003e\n \n \u003ch2\u003eCapabilities of the API\u003c\/h2\u003e\n \u003cp\u003eThe API enhances productivity and the predictive accuracy of text corpus management by allowing users to:\u003c\/p\u003e\n \u003cul\u003e\n \u003cli\u003eModify topic distribution properties post-creation; for example, updating the name or description of the distribution.\u003c\/li\u003e\n \u003cli\u003eRefine the output of existing topic distributions by adjusting thresholds or\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/body\u003e"}