<prompt> Bapisa le ho bapisa liphapang lipakeng tsa linear le logistic regression.nan  <response>: Instruction in English: Compare and contrast the differences between linear and logistic regression. . Response in English: Linear Regression and Logistic Regression are two popular machine learning algorithms used for predictive analysis, but they are different in their approach and application.Linear Regression is a supervised machine learning algorithm mainly used to predict continuous dependent variables based on one or more independent variables. In this algorithm, a linear function is created mapping the independent and dependent variables. The coefficients of the function are obtained by minimizing the mean squared error.Logistic Regression, on the other hand, is used when the dependent variable is categorical, or when predicting the probability of a binary outcome such as success or failure. In this algorithm, the logistic or sigmoid function is applied to the linear combination of independent variables to limit the output between 0 and 1. The coefficients are obtained by maximizing the likelihood of the data.The key differences between Linear and Logistic Regression are:- Linear Regression models a linear relationship between independent and dependent variables whereas Logistic Regression models the logarithm of the odds of the dependent variable.- Linear Regression is used for predicting continuous variables while Logistic Regression is used for classification and predicting categorical variables.- The Linear Regression model assumes that the errors are normally distributed whereas Logistic Regression does not have this assumption.- The output of a Linear Regression model is a continuous value while the output of a Logistic Regression model is a probability or binary outcome. . Response in Sesotho: Linear Regression le Logistic Regression ke li-algorithms tse peli tse tsebahalang tsa ho ithuta tsa mochini tse sebelisetsoang tlhahlobo ea esale pele, empa li fapane ka mokhoa oa tsona le tsebeliso. Linear Regression ke algorithm ea ho ithuta ka mochini e hlokometsoeng haholo-holo e sebelisoang ho bolela esale pele mefuta e sa fetoheng e itšetlehileng ka mofuta o le mong kapa tse ngata tse ikemetseng. Ho algorithm ena, tsebetso ea linear e etsoa ho etsa mapa oa mefuta e ikemetseng le e itšetlehileng ka eona. Li-coefficients tsa tšebetso li fumanoa ka ho fokotsa phoso e bolelang lisekoere. Logistic Regression, ka lehlakoreng le leng, e sebelisoa ha phapang e itšetlehileng e le ea lihlopha, kapa ha ho bolela esale pele monyetla oa sephetho sa binary joalo ka katleho kapa ho hloleha. Ho algorithm ena, tsebetso ea logistic kapa sigmoid e sebelisoa ho motsoako oa linear oa mefuta e ikemetseng ho fokotsa tlhahiso pakeng tsa 0 le 1. Li-coefficients li fumanoa ka ho eketsa menyetla ea boitsebiso. Liphapang tse ka sehloohong lipakeng tsa Linear le Logistic Regression ke: - Meetso ea Linear Regression kamano ea mola lipakeng tsa mefuta e ikemetseng le e itšetlehileng athe Logistic Regression e etsa mohlala oa logarithm ea maemo a fapaneng a itšetlehileng. - Linear Regression e sebelisoa bakeng sa ho bolela esale pele mefuta e tsoelang pele ha Logistic Regression e sebelisoa bakeng sa ho arola le ho bolela esale pele mefuta-futa ea lihlopha. - Mohlala oa Linear Regression o nka hore liphoso hangata lia ajoa athe Logistic Regression ha e na monahano ona. - Sephetho sa mohlala oa Linear Regression ke boleng bo tsoelang pele ha tlhahiso ea mohlala oa Logistic Regression e le monyetla kapa sephetho sa binary. 