<prompt> Bapisa melemo le likotsi tsa mekhoa e meli e tsebahalang ea ho ithuta ka mochini e atamelang ho theosa le lifate tsa liqeto.nan  <response>: Instruction in English: Compare the advantages and disadvantages of the two popular machine learning approaches linear regression and decision trees. . Response in English: Linear Regression and Decision Trees are two popular approaches to building predictive models in machine learning. They have their own advantages and disadvantages, and the choice between the two depends on the particular problem at hand.Advantages of Linear Regression:1. Simplicity: Linear regression models are simple to understand and interpret.2. Speed: Linear regression models can be trained relatively fast even on large datasets.3. Good for Predicting Numeric Output: Linear Regression is good for predicting numeric output or continuous variables such as weight or price.Disadvantages of Linear Regression:1. Linearity Assumption: Linear regression assumes that the relationship between the independent and dependent variables is linear. This may not always be the case in real-world scenarios.2. Sensitivity to Outliers: Linear regression models are also sensitive to outliers which can skew the results and reduce model accuracy.Advantages of Decision Trees:1. Easy to Understand: Decision trees are simple to understand and interpret. The tree structure represents the decision-making process in a visual and intuitive way.2. Non-Parametric: Decision trees are non-parametric, meaning that they do not make any assumptions about the underlying data distribution.3. Handles Categorical Variables: Decision trees can easily handle categorical variables without the need for encoding.Disadvantages of Decision Trees:1. Prone to Overfitting: Decision trees are prone to overfitting, especially when the tree is deep. This can reduce model accuracy on unseen data.2. Tendency to Bias: Decision trees have a tendency to bias towards variables with more levels. This can lead to sub-optimal splitting and reduced model accuracy.In conclusion, both linear regression and decision trees have their advantages and disadvantages. Linear regression is a simple and fast model that works well for predicting numeric outputs while decision trees are easy to understand, non-parametric and can handle categorical variables well. However, linear regression assumes linearity and is sensitive to outliers while decision trees are prone to overfitting and can bias towards variables with more levels. The choice between the two approaches depends on the specifics of the problem at hand. . Response in Sesotho: Linear Regression le Lifate tsa Qeto ke mekhoa e meli e tsebahalang ea ho aha mefuta e boletsoeng esale pele thutong ea mochini. Ba na le melemo le mathata a bona, me khetho pakeng tsa tse peli e itšetlehile ka bothata bo itseng bo teng. Melemo ea Linear Regression: 1. Bonolo: Mehlala ea ho khutlela morao e bonolo ho e utloisisa le ho e hlalosa. 2. Lebelo: Mehlala ea ho khutlela morao e ka koetlisoa ka potlako esita le ho li-dataset tse kholo. 3. Molemo bakeng sa ho Predicting Numeric Output: Linear Regression e ntle bakeng sa ho bolela esale pele palo ea palo kapa mefuta e tsoelang pele e kang boima kapa theko. Mathata a ho theoha ha Linear: 1. Maikutlo a Linearity: Ho theoha ha mola ho nka hore kamano pakeng tsa mefuta e ikemetseng le e itšetlehileng ka eona ke ea mola. Sena se kanna sa se be joalo kamehla maemong a nnete a lefatse. 2. Sensitivity to Outliers: Mehlala ea ho theoha ha linear e boetse e na le maikutlo a matle ho li-outliers tse ka khopamisang liphetho le ho fokotsa ho nepahala ha mohlala. Melemo ea Lifate tsa Qeto: 1. Ho Bonolo ho Utloisisa: Lifate tsa liqeto li bonolo ho utloisisa le ho li hlalosa. Sebopeho sa sefate se emela mokhoa oa ho etsa liqeto ka tsela e bonahalang le e utloahalang. 2. Non-Parametric: Lifate tsa liqeto ha li na parametric, ho bolelang hore ha li etse maikutlo leha e le afe mabapi le kabo ea data ea motheo. 3. E sebetsana le Mefuta e sa tšoaneng ea Categorical: Lifate tsa liqeto li ka sebetsana habonolo le mefuta-futa ea lihlopha ntle le tlhokahalo ea khouto. Mefokolo ea Lifate tsa Liqeto: 1. E Tloaelehile ho Feta Ho Fetisisa: Lifate tsa liqeto li na le tšekamelo ea ho feta, haholo-holo ha sefate se tebile. Sena se ka fokotsa ho nepahala ha mohlala ho data e sa bonoeng. 2. Tloaelo ea Leeme: Lifate tsa liqeto li na le tšekamelo ea ho ba leeme ho mefuta e nang le maemo a mangata. Sena se ka lebisa ho karohano e ntle ka ho fetesisa le ho fokotsa ho nepahala ha mohlala. Qetellong, lifate tse peli tsa ho theoha le ho etsa liqeto li na le melemo le melemo ea tsona. Linear regression ke mohlala o bonolo le o potlakileng o sebetsang hantle bakeng sa ho bolela esale pele liphetho tsa lipalo ha lifate tsa liqeto li le bonolo ho li utloisisa, ha li na parametric mme li khona ho sebetsana le mefuta e fapaneng hantle. Leha ho le joalo, linear regression e nka linearity me e na le kutloelo-bohloko ho barekisi ha lifate tsa liqeto li na le tšekamelo ea ho fetisoa ho feta tekano me li ka ba leeme ho mefuta e nang le maemo a mangata. Khetho pakeng tsa mekhoa e meli e itšetlehile ka lintlha tse tobileng tsa bothata bo teng. 