<prompt> Hlalosa hore na tsebetso ea ho emisa mokhoa oa ho ithuta oa mochini ke efe.nan  <response>: Instruction in English: Explain what is the process of standardization in a machine learning model. . Response in English: Standardization is a preprocessing technique used in machine learning to transform the features in the data into a standard scale. This is typically done to ensure that all the features have the same scale, and that no one feature dominates the others due to differences in their units or ranges.The process of standardization involves calculating the mean and standard deviation for each feature in the dataset. Once these values are calculated, each value in the dataset is transformed by subtracting the mean from it and then dividing the result by the standard deviation. This is done for each feature in the dataset, resulting in a standardized dataset where the values for each feature have a mean of 0 and a standard deviation of 1.Standardization is useful when the features in the dataset have different units or ranges, as it allows the model to compare the features on the same scale. This can improve the performance of the model by preventing features with large ranges from dominating the others. Additionally, many machine learning algorithms, such as linear regression, logistic regression, and support vector machines, make assumptions about the distribution of the data and perform better when the data is standardized.In summary, the process of standardization in a machine learning model involves transforming the features in the dataset to have a mean of 0 and a standard deviation of 1. This is done to ensure that all features are on the same scale, and can improve the performance of many machine learning algorithms. . Response in Sesotho: Standardization ke mokhoa oa ho lokisa esale pele o sebelisoang ho ithuteng ka mochini ho fetola likarolo tsa data ho sekala se tloaelehileng. Hangata sena se etsoa ho etsa bonnete ba hore likarolo tsohle li na le tekanyo e lekanang, le hore ha ho na tšobotsi e laolang tse ling ka lebaka la phapang ea li-unit kapa mekhahlelo ea tsona. Tsebetso ea ho emisa e kenyelletsa ho bala moelelo le phapang e tloaelehileng bakeng sa karolo ka ngoe lethathamong la data. Hang ha litekanyetso tsena li baloa, boleng bo bong le bo bong ho dataset bo fetoloa ka ho tlosa moelelo ho eona ebe o arola sephetho ka ho kheloha ho tloaelehileng. Sena se etsoa bakeng sa karolo e ngoe le e ngoe ea dataset, e leng se etsang hore ho be le dataset e tloaelehileng moo litekanyetso tsa karolo ka ngoe li nang le moelelo oa 0 le phapang e tloaelehileng ea 1. Boemo ba maemo bo molemo ha likarolo tsa dataset li na le li-unit kapa mekhahlelo e fapaneng, joalo ka ha lumella mohlala ho bapisa likarolo ka tekanyo e tšoanang. Sena se ka ntlafatsa tsebetso ea mohlala ka ho thibela likarolo tse nang le mefuta e meholo ho laola tse ling. Ho feta moo, li-algorithms tse ngata tsa ho ithuta ka mochini, joalo ka ho fokotseha ha linear, regression ea logistic, le mochini oa li-vector tse tšehetsang, li etsa likhakanyo mabapi le kabo ea data mme li sebetsa betere ha data e lekantsoe. Ka kakaretso, tsebetso ea ho tsitsa ka mokhoa oa ho ithuta mochine o kenyelletsa ho fetola likarolo tsa dataset ho ba le moelelo oa 0 le ho kheloha ho tloaelehileng ha 1. Sena se etsoa ho netefatsa hore likarolo tsohle li lekana, me li ka ntlafatsa tsebetso ea li-algorithms tse ngata tsa ho ithuta mochini. 