<prompt> Precision-tobetsa mokhoa oa ho theola maemo.nan  <response>: Instruction in English: Precision-tune a logistic regression model. . Response in English: Here are the steps you can follow to precision-tune a logistic regression model:1. Data preparation: For logistic regression, its important that the data is correctly prepared. The dependent variable should be binary or categorical, and the independent variables should be continuous or categorical. Also, make sure to check for multicollinearity, as having highly correlated features can bias the model.2. Feature selection: Select the most important features to include in the model. This can be done using techniques such as Recursive Feature Elimination, LASSO regularization or by examining the correlation between independent variables and the dependent variable.3. Hyperparameter tuning: Tune the hyperparameters of the model to improve its performance. The two main hyperparameters for logistic regression are the regularization parameter C and the solver. You can use techniques such as grid search or random search to find the best combination of hyperparameters.4. Model training: Train the model using the selected features and hyperparameters.5. Model evaluation: Evaluate the performance of the model using metrics such as accuracy, precision, recall, and F1 score. You can also plot the ROC curve and calculate the AUC to assess the models ability to distinguish between positive and negative classes.6. Iterate: Repeat the above steps, making changes to the feature selection and hyperparameters until you achieve the desired level of precision. . Response in Sesotho: Mehato eo u ka e latelang ke ena ho lokisa mokhoa oa ho busetsa morao ka mokhoa o nepahetseng. Phapang e itšetlehileng ka eona e lokela ho ba binary kapa karohano, me mefuta e ikemetseng e lokela ho ba e tsoelang pele kapa ea lihlopha. Hape, etsa bonnete ba hore u hlahloba multicollinearity, kaha ho ba le likarolo tse amanang haholo ho ka ba leeme la mohlala. 2. Khetho ea likarolo: Khetha likarolo tsa bohlokoa ka ho fetisisa ho kenyelletsa moetsong. Sena se ka etsoa ho sebelisoa mekhoa e kang Recursive Feature Elimination, LASSO regularization kapa ka ho hlahloba kamano pakeng tsa mefuta e ikemetseng le phapang e itšetlehileng ka eona. 3. Tokiso ea Hyperparameter: Tobetsa li-hyperparameter tsa mohlala ho ntlafatsa tsebetso ea eona. Li-hyperparameter tse peli tse ka sehloohong bakeng sa ho fokotseha ha lintho ke parameter ea kamehla C le solver. U ka sebelisa mekhoa e kang ho batla marang-rang kapa ho batla ka tšohanyetso ho fumana motsoako o motle oa li-hyperparameter. 4. Koetliso ea mohlala: Koetlisa mohlala ho sebelisa likarolo tse khethiloeng le li-hyperparameter. 5. Tlhahlobo ea mohlala: Lekola tšebetso ea mohlala u sebelisa metrics joalo ka ho nepahala, ho nepahala, ho hopola, le lintlha tsa F1. U ka boela ua rera lekhalo la ROC le ho bala AUC ho lekola bokhoni ba mohlala ba ho khetholla lipakeng tsa lihlopha tse ntle le tse mpe. 6. Iterate: Pheta mehato e ka holimo, u etse liphetoho ho khetho ea likarolo le li-hyperparameter ho fihlela u finyella boemo bo lakatsehang ba ho nepahala. 