<prompt> Re ka ntlafatsa tsebetso ea mohlala oa linear regression joang?nan  <response>: Instruction in English: How can we improve the performance of a linear regression model? . Response in English: Here are a few ways you can improve the performance of a linear regression model:1. Feature selection: Selecting the right features for our model is an important step towards improving its performance. Using domain knowledge or correlation analysis, remove irrelevant or redundant features that add no predictive value to the model.2. Regularization: Regularization adds a penalty term to the loss function, discouraging the coefficients from becoming too large. The two most common methods are Ridge Regression L2 regularization and Lasso Regression L1 regularization. This can help address problems such as overfitting and multicollinearity.3. Outlier detection: Outliers are data points that significantly differ from the other data points. Identify and remove outliers, as they can have a large impact on the regression line and lower the accuracy of the model.4. Data Transformation: Linear regression assumes that the relationship between the dependent and independent variables is linear. If that assumption isnt met, consider transforming the data to make the relationship more linear. Common transformations include taking the log, square root, or reciprocal of the dependent variable.5. Polynomial features: If a linear model isnt fitting the data well, try adding polynomial features to capture any non-linearity in the relationship between the dependent and independent variables. However, be careful not to overfit the data.6. Scaling of features: Standardize the features so that they have the same scale. This is important for the performance of the regularization methods, since they add a regularization term based on the size of the coefficients.Finally, its important to remember that linear regression has its limits. If these techniques dont help, consider using a more advanced model such as a decision tree regressor or a neural network. . Response in Sesotho: Mona ke litsela tse maloa tseo u ka ntlafatsang tsebetso ea mohlala oa linear regression: 1. Khetho ea likarolo: Ho khetha likarolo tse nepahetseng bakeng sa mohlala oa rona ke mohato oa bohlokoa ho ntlafatsa tshebetso ea oona. U sebelisa tsebo ea marang-rang kapa tlhahlobo ea khokahano, tlosa likarolo tse sa hlokahaleng kapa tse sa hlokahaleng tse eketsang boleng bo sa lebelloang ho mohlala. 2. Regularization: Regularization e eketsa nako ea kotlo mosebetsing oa tahlehelo, e nyahamisa li-coefficients ho ba kholo haholo. Mekhoa e meli e tsebahalang haholo ke Ridge Regression L2 regularization le Lasso Regression L1 regularization. Sena se ka thusa ho rarolla mathata a kang overfitting le multicollinearity. 3. Tlhahlobo ea kantle: Lintlha tse kantle ke lintlha tse fapaneng haholo le lintlha tse ling. Khetholla le ho tlosa li-outliers, kaha li ka ba le tšusumetso e kholo moleng oa ho khutlela morao le ho theola ho nepahala ha mohlala. 4. Phetoho ea Lintlha: Poelo ea morao-rao e nka hore kamano pakeng tsa liphapang tse itšetlehileng le tse ikemetseng ke mola. Haeba maikutlo ao a sa finyelloe, nahana ka ho fetola datha ho etsa hore kamano e be bonolo haholoanyane. Liphetoho tse tloaelehileng li kenyelletsa ho nka log, square root, kapa reciprocal of the dependent variable. 5. Likarolo tsa Polynomial: Haeba mofuta oa linear o sa lokele lintlha hantle, leka ho kenya likarolo tsa polynomial ho tšoara ho se lumellane hofe kapa hofe kamanong pakeng tsa mefuta e itšetlehileng ka eona le e ikemetseng. Leha ho le joalo, ela hloko hore u se ke ua fetelletsa data. 6. Ho lekanya likarolo: Emisa likarolo hore li be le sekala se lekanang. Sena ke sa bohlokoa bakeng sa tsebetso ea mekhoa e tloaelehileng, kaha ba eketsa nako ea ho tsitsa ho latela boholo ba li-coefficients. Qetellong, ho bohlokoa ho hopola hore ho fokotseha ha mela ho na le meeli. Haeba mekhoa ena e sa thuse, nahana ka ho sebelisa mohlala o tsoetseng pele haholoanyane joalo ka regressor ea sefate sa liqeto kapa neural network. 