{"id":5092,"date":"2023-09-01T14:46:08","date_gmt":"2023-09-01T12:46:08","guid":{"rendered":"https:\/\/www.kode-solutions.net\/?page_id=5092"},"modified":"2024-07-31T11:30:39","modified_gmt":"2024-07-31T09:30:39","slug":"fast","status":"publish","type":"page","link":"https:\/\/www.kode-solutions.net\/it\/prodotti-e-framework-di-ai\/alchemy\/fast\/","title":{"rendered":"Fast"},"content":{"rendered":"\r\n\t
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Feature Selection
<\/br>nella modellazione QSAR<\/h1>\n<\/div><\/div>\n\n\n\n

FAST, uno strumento basato sull\u2019approccio full-search per eseguire rapidamente la selezione delle caratteristiche, \u00e8 il primo software di una suite progettata per guidare chimici computazionali, laboratori e aziende lungo l’intero processo di modellazione QSAR<\/p>\n\n\n\n

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Scopri di pi\u00f9 su QUEEN<\/a> e l’intera Suite ALChemy<\/a><\/p>\n<\/div>\n<\/body><\/html>\n\n\n

\"\"<\/img><\/figure>\n<\/body><\/html><\/div>\n<\/div>\n\n\t\t\t\t\t<\/div>\r\n\t<\/div>\r\n\r\n\t\n\n\t\t
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 Vuoi saperne di pi\u00f9? Prenota una presentazione con il nostro team <\/p>\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t

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Features Selection: un passaggio fondamentale nel controllo della qualit\u00e0 di un modello di machine learning<\/h2>\n\n\n\n

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Durante la creazione di un modello di machine learning, l’identificazione di un sottoinsieme appropriato di features gioca un ruolo cruciale. Nella modellazione QSAR, in cui la quantit\u00e0 di descrittori molecolari disponibili nel dataset iniziale \u00e8 enorme, questo passaggio \u00e8 quello che influenza maggiormente le prestazioni finali. Poich\u00e9 in letteratura non \u00e8 stato individuato un criterio per determinare a priori l\u2019approccio migliore tra i tanti disponibili, abbiamo sviluppato uno strumento che ne utilizza sistematicamente molti e sceglie quello che, caso per caso, mostra la migliore performance<\/mark><\/p>\n\n\n\n

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FAST \u00e8 il primo strumento della suite che Kode Chemoinformatics ha sviluppato sviluppando per supportare chimici computazionali, laboratori e organizzazioni nei settori chimico, farmaceutico, alimentare e delle biotecnologie. Il cliente viene quindi guidato attraverso l’intero processo di creazione, gestione e implementazione dei modelli QSAR, dalla selezione delle caratteristiche alla predizione.<\/p>\n<\/div>\n<\/body><\/html><\/div>\n\n\t\t\t\t\t<\/div>\r\n\t<\/div>\r\n\r\n\t\n\n\r\n\t

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Un percorso passo-passo per la selezione delle funzionalit\u00e0<\/h2>\n\n\n\n


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Al fine di garantire il miglior sottoinsieme di features, minimizzando l’errore previsionale, FAST affronta la Featiires Selection attraverso tre passaggi sequenziali. Ogni passaggio ha costo computazionale e rigore crescenti, riducendo progressivamente la dimensione del dataset. L’approccio sequenziale consente di concentrare lo sforzo computazionale di ogni passaggio su un adeguato set di dati, rendendo il nuovo dataset accessibile per il passaggio successivo. Il tempo risparmiato consente di testare un gran numero di soluzioni diverse anche su un normale laptop. Infine, FAST calcola la qualit\u00e0 di ciascun set in termini di prestazioni delle previsioni attraverso uno o pi\u00f9 modelli di machine learning.<\/p>\n<\/div>\n<\/body><\/html>\n\n\n

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Il metodo dei tre passaggi<\/h2>\n<\/div>\n\n\n\n
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FAST identifica un insieme di soluzioni. Ciascuna di esse \u00e8 accompagnata da un indice di Accuracy, che permette di determinare quale set funziona meglio. Questo risultato si ottiene con un metodo in tre fasi<\/p>\n\n\n\n

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\r\n\t\t\t\t\t\t\tFiltering\t\t\t\t\t\t\t\t\t\t\t\t\t<\/h3>\r\n\t\t\t\t\t<\/div> \r\n\t\t\t\t\t
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Rimozione di quelle features che sono ridondanti o che presentano caratteristiche non compatibili con la modellazione <\/p>\t\t\t\t\t\t

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Rimozione di tutte le caratteristiche con importanza bassa o insufficiente all’interno dei modelli <\/p>\t\t\t\t\t\t

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Selezione di features, con convalida incrociata, che garantiscono il compromesso ottimale tra bias e varianza <\/p>\t\t\t\t\t\t

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Hai domande sui nostri prodotti?<\/p>\n

Scrivici via email!<\/p>\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t

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