<prompt> Hlalosa mohopolo oa phokotso ea boholo sebakeng sa thuto ea mochini ka mohlala o loketseng.nan  <response>: Instruction in English: Explain the concept of a dimension reduction in a machine learning setting with suitable example. . Response in English: Dimension reduction is a technique used in machine learning to reduce the number of input variables or features in a dataset while retaining the most important information. This is achieved by transforming the high dimensional data into a lower dimensional space. The goal is to identify and remove the redundant or irrelevant features and keep the most relevant ones while minimizing the loss of useful information. This can help improve the performance and accuracy of machine learning models, reduce the time required for training, and make data visualization easier.For example, consider a dataset that contains the sales records of a store, including information on customer demographics, behavior, and purchase history. If the goal is to build a model to predict customer purchases, it might be useful to reduce the number of input variables to speed up the training process and improve the models performance. Using dimension reduction techniques such as Principal Component Analysis PCA, one can identify the most important features that contribute to the variation in the data and retain them while discarding the others. In this case, features such as customer income, age, and purchase history might be identified as the most important and retained, while features such as customers phone number or address might be discarded as irrelevant. . Response in Sesotho: Phokotso ea boholo ke mokhoa o sebelisoang thutong ea mochini ho fokotsa palo ea lintho tse kentsoeng kapa likarolo ho pokello ea data ha o ntse o boloka tlhahisoleseling ea bohlokoahali. Sena se finyelloa ka ho fetola data ea boemo bo phahameng sebakeng se tlaase. Sepheo ke ho khetholla le ho tlosa likarolo tse sa hlokahaleng kapa tse sa hlokahaleng le ho boloka tse bohlokoa ka ho fetisisa ha u ntse u fokotsa tahlehelo ea tlhahisoleseding e molemo. Sena se ka thusa ho ntlafatsa tsebetso le ho nepahala ha mefuta ea ho ithuta ka mochini, ho fokotsa nako e hlokahalang bakeng sa koetliso, le ho nolofatsa pono ea data. Mohlala, nahana ka pokello ea data e nang le lirekoto tsa thekiso ea lebenkele, ho kenyeletsoa tlhahisoleseling mabapi le palo ea bareki, boitšoaro le nalane ea theko. Haeba sepheo e le ho aha mohlala ho bolela esale pele theko ea bareki, ho ka na ha e-ba molemo ho fokotsa palo ea mefuta-futa ea ho kenya letsoho ho potlakisa tsebetso ea koetliso le ho ntlafatsa tshebetso ea mohlala. Ho sebelisa mekhoa ea ho fokotsa litekanyo tse kang Principal Component Analysis PCA, motho a ka khetholla likarolo tsa bohlokoa ka ho fetisisa tse tlatsetsang ho fapana ha data le ho li boloka ha a ntse a lahla tse ling. Tabeng ena, likarolo tse kang chelete ea bareki, lilemo, le histori ea theko li ka na tsa tsejoa e le tsa bohlokoa ka ho fetisisa le tse bolokiloeng, ha likarolo tse kang nomoro ea mohala ea moreki kapa aterese li ka lahloa e le tse sa hlokahaleng. 