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4 Machine Kudzidza algorithms iwe yaunofanirwa kuziva nezvayo

machine learning

Machine kudzidza yasvika padanho idzva rose nekufamba kwenguva. Nekudaro, hapana chinhu senge imwechete algorithm kana zvasvika kune machine learning. Pamusoro pezvo, kuwedzerwa kweimwe nzira dzakadai seNLP uye neural network, Machine kudzidza yasvika pakakwirira. Kunyangwe makambani ari kusarudza iyi tekinoroji yepamusoro. Vari kuzvigamuchira iyo Machine kudzidza kushandisa michina and algorithms.

Chikonzero chikuru shure kweiyi data inoshandiswa nemakambani. Nekudaro, inoda yepamusoro uye yakanakisa algorithm yekuichengeta zvese. Kana iwe ukavhiringidzika nema algorithms mazhinji kunze uko saka heano ekumusoro algorithms e Machine kudzidza kutevera.

Kudzidza kusingatarisirwe

Mune iyi mhando yealgorithm, macomputer akachenjera anoshandiswa anogona nyore kushanda pane iyo data isina kunyorwa. Zvese zvavanoda kuti vanzwisise iyo pateni iyo data inochengetwa. Izvi zvichavabatsira kuti vanzwisise iyo chaiyo data isina mudzidzisi. Rudzi urwu rwealgorithm runonyanya kushandiswa kana bhuku rekubatsira risingabatsiri. Nekudaro, iyo system inosvetuka kumusoro uye kupedzisa iyo yese maitiro.

Iyi mhando yemhando inoenderana nerondedzero yekuenzanisira uye kuona pateni. Asi haina chero mhando yemazita kana zvikamu zvekuburitsa izvo zvakaita kuti zvienderane nehukama hwemuenzaniso. Iko kune yekuisa data iyo inoshandiswa kune iyi algorithm iyo inobatsira mukutonga kwekuchera, kuunganidza kana kupfupisa mapoinzi e data uye kunobatsira mukuonekwa kwepateni.

Hence, it easily derives insights and helps in analyzing the data for better results. In addition to this, this type of descriptive model includes different algorithm types. Some of them are Association rule kudzidza algorithms and clustering algorithms.

Inotarisirwa kudzidza

Mune rudzi urwu rwe kudzidza concept, one requires the function approximation. Here algorithms are worked up and the one that gives accurate results in selected for a better approach. Our aim is to ensure that the input value is described in the best possible way along with the estimation value of the data that is represented with X.

Hence, the time is invested in coming up with an accurate prediction that can help in understanding the function. Also, the manual assumptions are required in this type of data set, unlike the unsupervised kudzidza method. The manual work is mainly on the task to set it in such a way that a computer can understand the data points. You can take it as a teacher feeding in the instructions so that the computer can easily understand the language.

Izvi zvinobatsira mukufungidzira kukosha kwekuisa nekunyatso kuwana izvo zvinobuda. Nekudaro, pateni inofanirwa kuve yakajeka kuve nechokwadi chekuvimbika. Iwe unogona kutaura kuti inoshanda nemazvo nehukama kana hukama hwemuenzaniso. Izvo zvinoita sekuburitsa kukosha uko kunopihwa kubva kutsva yakavakirwa dhata iyo inopihwa neakamberi dhatabheti. Kunze kwedata rakanyorwa, kufungidzira kwekuenzanisira uye matambudziko ekumisikidza, pane imwe algorithm inowira pasi peichi chikamu. Imwe yadzo inoshandiswa zvakanyanya se:

a. Kudzoreredza kwemutsara

Iyo inozivikanwa uye yakakwenenzverwa algorithm - mutsara kudzoreredza - inoshandiswa zvakanyanya kune huwandu hwenhamba yezvinangwa. Kubva pakagadzwa Machine kudzidza, rave kunyanya kufarirwa. Chinhu chakanakisa nezverudzi urwu rwealgorithm ndechekuti inokwanisa kuenzanisa kuongorora hukama pakati pemhando mbiri dzakasiyana. Zvakare, zviri nyore kuongorora mafambiro kana shanduko imwe chete yaitwa muhurongwa.

However, this algorithm is not much considered by big bhizimisi tycoons. But for the start-ups, medium level or small businesses, it is similar to blessing in disguise. This is mainly used for the prediction of the team growth and forecasting revenue. For the purpose of kufanotaura modelling, ndeimwe yemhando yepamusoro yekutora iyo inogona kudzikisira njodzi yekukundikana. Pamusoro pezvo, hapana chikonzero chekuisa dhende muhomwe dzako.

Iyo inomiririrwa muchimiro chey equation iyo ine mutsara unoratidzira hukama pakati pechabuda chinobuda (y) uye chinongedzo chekuisa (x). Pano, iyo chaiyo coefficient kana Beta inoshandiswa pakumiririra kwekuisa kusiyanisa.

b. Naive Bayes

Rudzi urwu rwehurongwa rwakasiyana nekudzoserwa kwemutsara sezvo iine mamirioni e data museti iyo inogona kunge iri span. Nekudaro, maviri akakosha mhando mafomu anoshandiswa mune ino kesi:

  • Multinational Naïve Bayes - inoshandiswa kune iyo data inoparadzirwa nenzira dzakasiyana-siyana.
  • Gaussian Naïve Bayes - inoshandiswa pakugovana kunoitwa zvakajairika kugoverwa kweiyo kukosha kwekuenderera.

Iyi ndiyo yakanaka mhando yekutaura iyo inoshandiswa kune mamwe epamusoro basa basa.

  • Google use this type of technique to filter out span data or Website.
  • Google yakashandisawo iyi algorithm nzira yavo yekugonesa nzira. Iyo inobatsira muPageRank index iyo inogona nyore kupatsanura iro gwaro rose.
  • Facebook haisi zvakare kure nekuti inoshandiswa iyo iyo manzwiro ongororo. Mune izvi, mamiriro emamiriro ezvinhu anowanikwa.

Kunze kweizvi, kune mamwe maalgorithms akaita kuti nzira iyi ive yakakosha:

  • Vavakidzani vepedyo
  • Tsigira Vector Machines
  • Sarudzo Muti

Maitiro evose akada kufanana nemisiyano midiki.

c. Yekusimbisa Kudzidza

Iyi mhando yenzira inoenderana nekubatana kwenzvimbo iyo inobatsira mukuunganidza data rakakodzera. Nekudaro, zvinova nyore kuuya nezviito chaizvo kuti zvikwanisike kudzikisira mwero wenjodzi uye kuwedzeredza mhedzisiro yacho. Muizvi mhando yealgorithm, mumiriri akashandisa iyo inoteedzera iwo mhepo nguva dzose mune iterative fomu. Izvo zvinokwanisika kudzidza zvidiki zvidiki kubva kune iyo sisitimu yese kudzamara zvabuda zvawanikwa.

Iko rudzi rwe Machine kudzidza algorithm is also a part of chakagadzirwa njere that helps in determining the behavior of an agent. The context is accurate to ensure that there is no risk and the performance of the system can be maximized. This system used the deep adversarial network. Temporal difference and Q-kudzidza algorithms.

d. Semi-inotarisirwa kudzidza

Kana isu tikacherekedza yakatariswa kana isina kuchengetedzwa algorithm ipapo iwe unogona kunzwisisa kuti zvinyorwa zvinoshandiswa kana kuti hazvishandiswe uchicherechedza iyo yese system. Nekudaro, mune semi-inotarisirwa nharaunda, ndiyo chikamu cheese masisitimu. Nekuda kwekukanganisa kwevanhu, mutengo weiyo label wakanyanya kwazvo izvo zvakaita kuti makambani asarudze iyo inodhura fomu yeiyi system.

Kune akawanda mavara asipo kubva kune mazhinji echikamu asi mashoma awo achiripo. Izvi zvakavaita chikamu cheiyo semi-inotarisirwa system. Nekudaro, inova mhinduro yakakodzera kune vatengi iyo inogona kushandiswa kuvaka modhi. Mumashoko akapusa, hazvizove zvakaipa kutaura kuti kunyangwe iyo data iri mumazita isingazivikanwe, ichine zvimwe zvemashoko akakosha. Iyo dhata rese richa kupihwa sekureva kweparamende ichipa mhedzisiro.

Kana zvasvika kuna Machine kudzidza algorithm, there are so many possibilities that we can use as criteria. However, if see the bigger picture then Machine kudzidza task will set out which mode of the algorithm is ideal for them. We can’t expect any of the data set to be used and considered as ideal.

Kutsvaga kweML kuitisa musangano rako? Taura nekutaurirana

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