Logistic nlọghachi azụ
Ntughari mgbagha na-ebu amụma ihe gbasara puru omume na ihe bụ nke klaasị, dị ka spam ma ọ bụ na ọ bụghị spam, site n'ịkwalite nchikota dị arọ site na usoro S.
Nchịkọta
It matters as the foundational, highly interpretable algorithm for classification.
Ime miri emi
N'agbanyeghị aha ya, mgbagha mgbagha bụ usoro nhazi ọkwa, ọ bụghị nke nlọghachi azụ. Ọ na-agbakọ nchikota nha nke njirimara ntinye, wee gafee uru ahụ site na ọrụ sigmoid (lọjiiki), nke na-esetịpụ nọmba ọ bụla na ihe gbasara nke puru omume n'etiti 0 na 1. Ọ bụrụ na ihe gbasara nke puru omume gafere ọnụ ụzọ, na-abụkarị 0.5, a na-akpọ isi ihe dị mma. Ihe nlereanya a na-amụta ịdị arọ ya site na ibelata mfu log (cross-entropy), bụ nke na-ata oke amụma na-ezighi ezi. Isi ike bụ nkọwa: ịdị arọ nke ọ bụla na-agwa gị ka otu njirimara si atụgharị na njedebe nke nsonaazụ ya, yabụ ị nwere ike ịhụ ihe ndị na-ebuli amụma elu ma ọ bụ ala. Ụdị Multiclass na-agbatị ya site na iji ọrụ softmax.
Nghọta nka nka
Ọrụ sigmoid, 1 kewara site na (1 gbakwunyere e na z na-adịghị mma), na-atụgharị akara akara z ka ọ bụrụ ihe gbasara omume. A na-azụ ụdị a site na mgbada gradient iji belata mfu-entropy, nke bụ convex, yabụ enwere otu kacha mma zuru ụwa ọnụ. Arọ ahụ nwere ihe dị ọcha: nke ọ bụla bụ mgbanwe na log-odds kwa nkeji nke njirimara ya, na ịkọwapụta ya na-enye oke erughị eru nke ndị ọkachamara ngalaba nwere ike ịkọwa ozugbo.
Mmetụta atụmatụ
Mkpebi doro anya
Ọ na-enyere gị aka ikewapụta nkwupụta ọrụ aka doro anya na asụsụ ahịa.
Ọnụ ego na mmefu ego
Ị nwere ike ịjụ ajụjụ mmejuputa iwu ka mma tupu itinye ego ma ọ bụ oge.
Team na usoro ọrụ
Ndị otu nwere nghọta na-eme ka ngwaahịa, amụma na mkpebi mmụta ka mma.
Ọdịnihu nke Logistic Regression
Ntughari mgbagha na-adịgide n'ihi na ọ na-adị ngwa ngwa, doo anya, yana ntọala siri ike nke a na-atụle ụdị ndị nwere mmasị na ya. N'ime ngalaba ahaziri dị ka ego na ọgwụ, nkọwa ya na-eme ka ọ na-arụsi ọrụ ike ebe ụdị igbe ojii na-eche nyocha ihu. Ọ na-ebikwa n'ime netwọkụ akwara nke ọgbara ọhụrụ: oyi akwa nkesa ikpeazụ nwere sigmoid ma ọ bụ softmax bụ n'ezie mgbagha mgbagha, yabụ ịghọta na ọ bụ ụzọ maka mmụta miri emi.
Mmejuputa n'ezie n'ụwa
Nchacha spam email: na-eche na ọ ga-ekwe omume ozi bụ spam sitere na okwu na njirimara onye izipu.
Ntụle kredit: ịkọ amụma o yikarịrị ka onye na-achọ mbinye ego ga-adabara, yana onyinye dị arọ doro anya.
Amụma ihe ize ndụ ahụike: na-atụle ohere onye ọrịa nwere ọrịa site na ụkpụrụ ule na akara ngosi.
Ụdị churn ahịa: ịkọ amụma ma onye ahịa ọ ga-akagbu ndenye aha n'ọnwa na-abịa.
Ihe ize ndụ & okporo ụzọ nche
Otu dị iche iche nwere ike iji otu okwu ahụ mee ihe n'ụzọ dị iche, yabụ kọwapụta oge n'oge.
Ihe nrịbama nwere ike ịdị ike ebe arụmọrụ ụwa na-adaghị adaba.
Ileghara ogo data na atụmatụ nyocha anya na-emepụtakarị nsonaazụ na-adịghị mma.
Map mmejuputa
Malite na nkọwa asụsụ dị larịị nke nsonaazụ ịchọrọ.
Họrọ otu metrik ịga nke ọma na otu ọnọdụ ọdịda tupu nnwale.
Gbaa obere onye na-anya ụgbọ elu nwere data nnọchite anya, ọ bụghị ihe ngosi ngosi na-egbu maramara.
Detuo ebe Logistic Regression na-enyere aka yana ebe ụzọ ndị dị mfe dị mma.
Nọgide na-eme nchọpụta
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Ntuziaka na-esote
AI na Logistics
Ajụjụ a na-ajụkarị
What is Logistic Regression?
Ntughari mgbagha na-ebu amụma ihe gbasara puru omume na ihe bụ nke klaasị, dị ka spam ma ọ bụ na ọ bụghị spam, site n'ịkwalite nchikota dị arọ site na usoro S. Ọ dị mkpa dị ka ntọala, algọridim enwere ike ịkọwa nke ukwuu maka nhazi ọkwa.
N'agbanyeghị aha ya, gịnị ka a na-eji regression logistic n'ezie?
Logistic regression na-ewepụta ihe puru omume ma jiri ya kewaa ihe n'ime klaasị pụrụ iche, dị ka spam na-abụghị spam.
Kedu ihe sigmoid (lọjiiki) na-arụ na ihe nlereanya?
Sigmoid na-akụghasị akara ahịrị n'ime oke 0-na-1 ka enwere ike ịgụpụta nsonaazụ ya dị ka ihe gbasara omume.
Kedu ọrụ mfu ka mgbagha mgbagha na-ebelata n'oge ọzụzụ?
A na-azụ mgbagha mgbagha site na ibelata cross-entropy, nke na-emebi atụmatụ ntụkwasị obi mana na-ezighi ezi.
Kedu ihe kpatara eji jiri mgbagha mgbagha bara uru maka nkọwa?
Ọnụọgụ ndị a mụtara nwere nkọwa doro anya: nke ọ bụla na-egosipụta mgbanwe na log-odds kwa nkeji nke njirimara ya, na-egosipụtakarị dị ka oke erughị ala.
Kedu ka nlọghachị ngwa ngwa si atụgharịkarị ihe gbasara omume ka ọ bụrụ akara klaasị?
Ọ bụrụ na ihe gbasara nke puru omume gafeta oke (nke na-abụkarị 0.5), a na-akpọ isi ihe dị mma; ma ọ bụghị ihe ọjọọ.