Hōʻike ka hōʻike hōʻoia maikaʻi o Syntho i ka ʻikepili synthetic i hana ʻia a hōʻike i ka pololei, pilikino, a me ka wikiwiki o ka ʻikepili synthetic i hoʻohālikelike ʻia me ka ʻikepili kumu.
Ma Syntho, maopopo mākou i ke koʻikoʻi o ka ʻikepili synthetic pono a pololei. ʻO ia ke kumu e hāʻawi ai mākou i kahi hōʻike hōʻoia maikaʻi no kēlā me kēia holo ʻikepili synthetic. Loaʻa i kā mākou hōʻike maikaʻi nā ana like ʻole e like me ka puʻunaue, correlations, multivariate distribution, privacy metrics, a me nā mea hou aku. Ma kēia ala, hiki iā ʻoe ke loiloi maʻalahi i ka ʻikepili synthetic a mākou e hāʻawi ai i ke ʻano kiʻekiʻe loa a hiki ke hoʻohana ʻia me ka pae like o ka pololei a me ka hilinaʻi e like me kāu ʻikepili kumu.
Ke kiʻi ʻana i kahi ʻike: hōʻike kēia ʻāpana i nā mea koʻikoʻi mai kā mākou hōʻike ʻike ʻikepili synthetic. Nānā kā mākou mau loiloi i ka ʻikepili synthetic i ka hoʻohālikelike ʻana me ka ʻikepili maoli ma nā ʻano like ʻole.
Synthetic Data Multivariate Distributions i ka hoʻohālikelike ʻana i ka ʻikepili maoli
ʻO nā māhele multivariate a me nā hoʻoponopono multivariate e lawe iā mākou ma waho aʻe o nā ana hoʻokahi, e hāʻawi ana i kahi ʻike piha i ka pili ʻana o nā ʻano like ʻole. Hoʻopili ka Syntho Engine i kēia mau pilina.
He paʻakikī ka hoʻokumu ʻana i ka ʻikepili synthetic a aia nā pitfalls a pono e hoʻomalu ʻia. Me nā algorithms AI, he pilikia ka overfitting a ʻo ia hoʻi ka hihia no ka hana ʻikepili synthetic me AI. No laila, pono e kaohi kekahi no ka pilikia o ka overfitting i ka wā e hana ai i ka ʻikepili synthetic. Hoʻomalu ʻia ka pilikia o ka overfitting ma ka Syntho Engine. Ma luna o kēlā, ʻo ka hōʻike ʻo Syntho Quality Assurance (QA) e ʻae i nā hui e hōʻike i ka ʻikepili synthetic ʻaʻole i overfit ma ka ʻikepili kumu. Hoʻopili pū mākou i nā mea pili pilikino hou aʻe, i hoʻohana pinepine ʻia e nā loiloi kūloko.
E ho'āʻo i ka "Paʻi pololei" me ka Identical Match Ratio (IMR)
ʻO ka hōʻike ʻana ʻaʻole i ʻoi aku ka nui o ka ratio o nā moʻolelo ʻikepili synthetic e pili ana i kahi moʻolelo maoli mai ka ʻikepili kumu ma mua o ka ratio hiki ke manaʻo ʻia i ka wā e nānā ana i ka ʻikepili kaʻaahi.
Hoʻāʻo ma “Nā pāʻani like” me ka Distance to Closest Record (DCR)
Hōʻike i ka mamao maʻamau no nā moʻolelo ʻikepili synthetic i kā lākou moʻolelo maoli kokoke loa i loko o ka ʻikepili kumu ʻaʻole i kokoke loa ma mua o ka mamao e hiki ke manaʻo ʻia i ka wā e nānā ana i ka ʻikepili kaʻaahi.
Hoʻāʻo ma "Outliers" me ka Kokoke loa Neighbor Distance Ratio (NNDR)
ʻO ka hōʻike ʻana ʻaʻole i ʻoi aku ka pili o ka lākiō ma waena o ka moʻolelo synthetic kokoke loa a me ka lua kokoke i kā lākou moʻolelo kokoke loa i loko o ka ʻikepili kumu ma mua o ka ratio e manaʻo ʻia no ka ʻikepili kaʻaahi.
He kiʻi paʻi wale nō kēia e hōʻuluʻulu ana i ke ʻano o kā mākou ʻimi ʻikepili synthetic a me ka hōʻike hōʻoia maikaʻi. Hāʻawi ia i kahi ʻike nuanced o ka puʻunaue, correlations, a me nā māhele multivariate ma ke ʻano he ʻāpana o ka ʻikepili synthetic e like me ka hopu ʻia e nā mana holomua o ka Syntho Engine. Loaʻa nā kikoʻī hou aku e pili ana i kā mākou hōʻike hōʻoia maikaʻi ma ke noi.