<prompt> Phapano ke efe lipakeng tsa ho theoha ha linear le logistic regression?nan  <response>: Instruction in English: What is the difference between linear regression and logistic regression? . Response in English: Linear regression and logistic regression are both types of predictive modeling techniques that are used to analyze the relationship between a dependent variable the output and one or more independent variables the inputs. Despite their similarities, there are several key differences between these two methods:- Type of Dependent Variable: The main difference between linear and logistic regression lies in the type of dependent variable being analyzed. Linear regression is used for predicting continuous outcomes, such as a stock price, weight, or temperature. In contrast, logistic regression is used for predicting binary outcomes - where there are only two possible outcomes, such as yes or no, pass or fail, 1 or 0.- Output interpretation: With linear regression, the output is a continuous value that represents the best-fit line in the case of simple linear regression or hyperplane in the case of multiple linear regression for the given data. The output can take any value within a given range. In contrast, logistic regression outputs a probability that the given input point belongs to a certain class. This probability must be between 0 and 1, and is obtained by running the linear regression formula through a sigmoid function.- Error Measurement: Linear regression uses the least squares method to minimize the sum of squared errors between the predicted and actual values. This method works well for continuous data, where the errors can take any value. However, for binary data, the errors can only take certain values i.e. 0 or 1, so the least squares method is not appropriate. Instead, logistic regression uses maximum likelihood estimation to find the best fit for the data. This method maximizes the probability of correctly predicting the observed data.- Linearity Assumption: Linear regression assumes that the relationship between the dependent and independent variables is linear, i.e., a change in the independent variable results in a proportional change in the dependent variable. In contrast, logistic regression does not assume a linear relationship between variables. Instead, it uses the logistic function to transform the output into a probability, which allows the model to handle non-linear relationships.In summary, linear and logistic regression are two different predictive modeling techniques that are used to analyze different types of dependent variables - continuous and binary, respectively. Linear regression outputs a continuous value, while logistic regression outputs a probability of belonging to a certain class. They also use different methods for measuring errors and have different assumptions about the linearity of the relationship between variables. . Response in Sesotho: Phokotso ea mola le mokhoa oa ho khutlela morao ke mefuta e meli ea mekhoa ea ho lekanya esale pele e sebelisetsoang ho sekaseka kamano lipakeng tsa phapano e itšetlehileng ka eona sehlahisoa le mofuta o le mong kapa tse ngata tse ikemetseng tse kenang. Ho sa tsotellehe ho tšoana ha tsona, ho na le liphapang tse maloa tsa bohlokoa pakeng tsa mekhoa ena e meli: - Mofuta oa Phapang e itšetlehileng ka eona: Phapang e kholo pakeng tsa ho khutlela morao ka mola le mokhoa o itšetlehileng ka mofuta o hlahlojoang. Linear regression e sebelisoa ho bolela esale pele liphetho tse tsoelang pele, joalo ka theko ea setoko, boima ba mele kapa mocheso. Ka lehlakoreng le leng, mokhoa oa ho theoha ha lintho o sebelisoa bakeng sa ho bolela esale pele liphetho tsa binary - moo ho nang le liphetho tse peli feela tse ka khonehang, tse kang e kapa che, pasa kapa feela, 1 kapa 0. - Tlhaloso ea sephetho: Ka mokhoa oa ho khutlela morao, tlhahiso ke boleng bo tsoelang pele bo emelang mohala o loketseng ka ho fetisisa tabeng ea mokhoa o bonolo oa linear regression kapa hyperplane tabeng ea ho fokotseha ha mela e mengata bakeng sa data e fanoeng. Sephetho se ka nka boleng bofe kapa bofe ka hara mefuta e fanoeng. Ka lehlakoreng le leng, ho fokotseha ha lintho ho hlahisa monyetla oa hore ntlha e fanoeng ke ea sehlopha se itseng. Monyetla ona o tlameha ho ba pakeng tsa 0 le 1, me o fumanoa ka ho sebelisa mokhoa oa ho khutlisa oa mola ka tšebetso ea sigmoid. - Tekanyo ea Phoso: Mothalo oa ho khutlela morao o sebelisa mokhoa o fokolang oa lisekoere ho fokotsa kakaretso ea liphoso tse nang le lisekoere lipakeng tsa boleng bo boletsoeng esale pele le ba nete. Mokhoa ona o sebetsa hantle bakeng sa data e tsoelang pele, moo liphoso li ka nkang bohlokoa leha e le bofe. Leha ho le joalo, bakeng sa data ea binary, liphoso li ka nka feela litekanyetso tse itseng ke hore 0 kapa 1, kahoo mokhoa o fokolang oa li-square ha o tšoanelehe. Ho e-na le hoo, regression ea lisebelisoa e sebelisa tekanyo e phahameng ea menyetla ho fumana lintlha tse nepahetseng ka ho fetisisa. Mokhoa ona o eketsa monyetla oa ho bolela esale pele ka nepo data e hlokometsoeng. - Maikutlo a Linearity: Ho theoha ha mola ho nka hore kamano pakeng tsa mefuta e itšetlehileng ka eona le e ikemetseng e na le mola, ke hore, phetoho ea phapang e ikemetseng e fella ka phetoho e leka-lekaneng ea phapang e itšetlehileng. Ka lehlakoreng le leng, ho fokotseha ha lintho ha ho nke kamano e pakeng tsa mefuta e fapaneng. Ho e-na le hoo, e sebelisa tsebetso ea lisebelisoa ho fetola tlhahiso hore e be monyetla, e lumellang mohlala ho sebetsana le likamano tse se nang moeli. Ka bokhutsoane, tatellano ea linear le logistic ke mekhoa e meli e fapaneng ea ho lepa mohlala e sebelisetsoang ho sekaseka mefuta e fapaneng ea mefuta e itšetlehileng ka eona - e tsoelang pele le ea binary, ka ho latellana. Linear regression e hlahisa boleng bo tsoelang pele, ha logistic regression e hlahisa monyetla oa ho ba setho sa sehlopha se itseng. Ba boetse ba sebelisa mekhoa e fapaneng ea ho lekanya liphoso me ba na le maikutlo a fapaneng mabapi le ho lumellana ha kamano pakeng tsa mefuta-futa. 