<prompt> Bapisa tsebetso ea mohlala oa linear regression le mohlala oa neural network.nan  <response>: Instruction in English: Compare the performance of a linear regression model with a neural network model. . Response in English: Linear regression and neural networks are both used for predictive modeling, but they differ in their structure and approach to solving problems.A linear regression model is a simple algorithm that fits a line to a set of data points in a way that minimizes the sum of the squared residuals. This model is best suited to problems where the relationship between the input and output variables is linear.On the other hand, a neural network is a multi-layered structure consisting of interconnected neurons, where the relationship between each layers neurons is modeled by weighted connections that are trained with data. Unlike linear regression, neural networks can model complex, nonlinear relationships between inputs and outputs, making them better suited to more complicated and diverse problems.In terms of performance, a linear regression model may perform better than a neural network on problems where the relationship between inputs and outputs is linear. However, if the relationship is more complicated, a neural network should be able to provide better results due to its ability to model complex, nonlinear relationships.Another important consideration is the size of the data set. A linear regression model is simpler and therefore, may be a better choice for a relatively small data set. Whereas, neural networks can benefit from larger data sets and may outperform linear regression when there is a lot of data available.In summary, the performance of a linear regression model versus a neural network depends on the nature of the problem and the data available. Both models can perform well, but in general, neural networks are more suited to complex, nonlinear problems, while linear regression is best for simpler, linear problems. . Response in Sesotho: Li-linear regression le neural network li sebelisoa ka bobeli bakeng sa mohlala o boletsoeng esale pele, empa li fapane ka sebopeho sa tsona le mokhoa oa ho rarolla mathata. Moetso oa linear regression ke algorithm e bonolo e lumellanang le mola ho sehlopha sa lintlha tsa data ka tsela e fokotsang kakaretso ea masala a squared. Moetso ona o loketse ka ho fetesisa mathata moo kamano lipakeng tsa mefuta e kenang le e hlahisoang e leng mela. Ka lehlakoreng le leng, marang-rang a methapo ea kutlo ke sebopeho se nang le mekhahlelo e mengata se nang le li-neurone tse hokahaneng, moo kamano pakeng tsa li-neuron tsa lera ka ngoe e etsisoang ke likhokahano tse boima tse koetlisitsoeng ka data. Ho fapana le ho fokotseha ha linear, marang-rang a neural a ka etsa mohlala oa likamano tse rarahaneng, tse se nang moeli pakeng tsa lintho tse kenang le tse hlahisoang, e leng ho li etsang hore li lokele mathata a rarahaneng le a fapaneng. Mabapi le tsebetso, mohlala oa linear regression o ka sebetsa hantle ho feta marang-rang a marang-rang a mathata moo kamano pakeng tsa lintho tse kenang le tse hlahisoang e leng mola. Leha ho le joalo, haeba kamano e rarahane haholoanyane, marang-rang a marang-rang a lokela ho khona ho fana ka liphello tse molemo ka lebaka la bokhoni ba eona ba ho etsa mohlala oa likamano tse rarahaneng, tse se nang moeli. Taba e ngoe ea bohlokoa ke boholo ba sete ea data. Mohlala oa linear regression o bonolo, ka hona, e ka ba khetho e ntle bakeng sa sete e nyane ea data. Leha ho le joalo, marang-rang a neural a ka rua molemo ho li-data tse kholoanyane me a ka feta mokhoa oa ho khutlela morao ha ho na le boitsebiso bo bongata bo fumanehang. Ka kakaretso, tsebetso ea mokhoa oa ho fokotsa mekhoa e metle khahlanong le neural network e itšetlehile ka mofuta oa bothata le lintlha tse fumanehang. Mefuta ena ka bobeli e ka sebetsa hantle, empa ka kakaretso, marang-rang a neural a loketse haholoanyane mathata a rarahaneng, a se nang moeli, athe ho fokotseha ha mela ho molemo ka ho fetisisa bakeng sa mathata a bonolo, a marang-rang. 