<prompt> Hlalosa likarolo tsa bohlokoa tsa tsamaiso e latelang.Sistimi: Sistimi ea ho ithuta ka mochini ho bolela esale pele boemo ba leholimo  <response>: Instruction in English: Identify the key components of the following system. . Response in English: The key components of a machine learning system to predict the weather may include the following: 1. Data collection: This involves gathering data from various sources such as weather stations, satellites, and historical records. The data typically includes variables like temperature, humidity, wind speed, and precipitation.2. Pre-processing: This involves cleaning and transforming the data to a suitable format for analysis, including dealing with missing values, outliers, and scaling the data.3. Feature engineering: This is the process of creating new features or variables from the raw data to improve the predictive power of the model.4. Model selection: There are multiple machine learning models that can be used for weather prediction, such as decision trees, neural networks, and regression. The selection of the appropriate model will depend on the specific problem and the data.5. Training: This involves feeding the data into the model and adjusting the models parameters to minimize the prediction error.6. Validation: This involves using a separate dataset, not used during training, to evaluate the performance of the model.7. Deployment: Once the model is validated, it can be deployed to make predictions on new data.8. Feedback loop: The system should have a feedback mechanism to collect new data and update the model to improve its performance over time.9. User interface: Lastly, the system could have an interface that provides an easy way for users to input data, view predictions, and receive notifications or alerts. . Response in Sesotho: Likarolo tsa bohlokoa tsa sistimi ea ho ithuta ka mochini ho bolela esale pele boemo ba leholimo li ka kenyelletsa tse latelang: 1. Pokello ea lintlha: Sena se kenyelletsa ho bokella lintlha ho tsoa mehloling e fapaneng joalo ka liteishene tsa leholimo, lisathelaete le lirekoto tsa nalane. Lintlha hangata li kenyelletsa lintho tse fapaneng joalo ka mocheso, mongobo, lebelo la moea, le pula. 2. Ho sebetsa esale pele: Sena se kenyelletsa ho hloekisa le ho fetola data ho sebopeho se loketseng bakeng sa tlhahlobo, ho kenyelletsa ho sebetsana le litekanyetso tse sieo, li-outliers, le ho lekanya lintlha. 3. Feature engineering: Ena ke mokhoa oa ho theha likarolo tse ncha kapa mefuta e fapaneng ho tsoa ho data e tala ho ntlafatsa matla a ho bolela esale pele a mohlala. 4. Khetho ea mohlala: Ho na le mefuta e mengata ea ho ithuta ka mochine e ka sebelisoang bakeng sa ho bolela esale pele boemo ba leholimo, joalo ka lifate tsa liqeto, marang-rang a methapo ea kutlo, le ho khutlela morao. Khetho ea mofuta o nepahetseng e tla itšetleha ka bothata bo itseng le data. 5. Koetliso: Sena se kenyelletsa ho fepa lintlha ho mohlala le ho lokisa liparamente tsa mohlala ho fokotsa phoso ea ho bolela esale pele. 6. Ho netefatsa: Sena se kenyelletsa ho sebelisa dataset e arohaneng, e sa sebelisoe nakong ea koetliso, ho hlahloba tsebetso ea mohlala. 7. Tšebeliso: Hang ha mohlala o tiisitsoe, o ka sebelisoa ho etsa likhakanyo ka lintlha tse ncha. 8. Feedback loop: Sistimi e lokela ho ba le mokhoa oa ho fana ka maikutlo ho bokella lintlha tse ncha le ho nchafatsa mohlala ho ntlafatsa tsebetso ea eona ka nako. 9. Sehokelo sa mosebelisi: Qetellong, sistimi e ka ba le sebopeho se fanang ka mokhoa o bonolo oa hore basebelisi ba kenye lintlha, ba shebe likhakanyo, le ho amohela litsebiso kapa litlhokomeliso. 