<prompt> Hlalosa melemo ea ho sebelisa mochini oa ho ithuta o itirisang bakeng sa ho etsa mohlala.nan  <response>: Instruction in English: Outline the advantages of using automated machine learning for predictive modelling. . Response in English: 1. Efficiency and speed: Automated machine learning processes can analyze large data sets and select the best models faster than humans. This significantly reduces the time required for model selection, data pre-processing, and feature engineering.2. Model accuracy: Automated machine learning algorithms are designed to choose the best model for a particular data set and fine-tune the parameters to achieve optimal results. This increases the accuracy of predictions as compared to manually selected models.3. Reduced human error: Since the process is automated, there is a reduced chance of human error in model selection, data pre-processing and feature engineering. This ensures more accurate results.4. Easy to use: Automated machine learning tools are designed to be user-friendly and do not require advanced data science or programming skills. This makes it easier for users who may not be experts in machine learning to create accurate predictive models.5. Cost effective: Automated machine learning reduces the need for human expertise in data science, which can be costly. It can also reduce the time required for data processing and analysis, which leads to cost savings.6. Scalability: Automated machine learning tools make it easier to scale predictive modeling activities, as the automation ensures that the processes are standardized and repeatable. This means that more data sets can be analyzed and more predictive models can be created in less time.7. Continuous improvement: Automated machine learning algorithms are designed to learn from data and improve their performance over time. This means that predictive models can be continuously refined and updated to ensure their accuracy, even as the underlying data changes. . Response in Sesotho: 1. Sebetsa le lebelo: Mekhoa ea ho ithuta ea mochine e ikemetseng e ka hlahloba lisebelisoa tse kholo tsa data le ho khetha mehlala e metle ka potlako ho feta batho. Sena se fokotsa haholo nako e hlokahalang bakeng sa khetho ea mohlala, tsebetso ea data pele, le boenjiniere ba likarolo. 2. Ho nepahala ha mohlala: Li-algorithms tsa ho ithuta ka mochine li etselitsoe ho khetha mohlala o motle ka ho fetisisa bakeng sa sete e itseng ea data le ho lokisa li-parameter ho finyella liphello tse nepahetseng. Sena se eketsa ho nepahala ha likhakanyo ha ho bapisoa le mefuta e khethiloeng ka letsoho. 3. Phoso ea batho e fokotsehileng: Kaha tsebetso e etsoa ka boiketsetso, ho na le monyetla o fokotsehileng oa phoso ea motho khethong ea mohlala, ho sebetsa pele ho data le boenjiniere ba likarolo. Sena se netefatsa liphetho tse nepahetseng haholoanyane. 4. Ho bonolo ho e sebelisa: Lisebelisoa tsa ho ithuta ka mochine li etselitsoe ho sebelisana le basebelisi me ha li hloke saense ea data e tsoetseng pele kapa tsebo ea ho etsa mananeo. Sena se nolofalletsa basebelisi bao e kanna eaba ha se litsebi tsa ho ithuta ka mochini ho etsa mefuta e nepahetseng ea ho bolela esale pele. 5. Theko e sebetsang: Ho ithuta ka mochine o ikemetseng ho fokotsa tlhokahalo ea botsebi ba batho ho saense ea data, e ka bitsang chelete e ngata. E ka boela ea fokotsa nako e hlokahalang bakeng sa ho sebetsa le ho hlahloba lintlha, e leng se lebisang ho bolokeng litšenyehelo. 6. Scalability: Lisebelisoa tse ikemetseng tsa ho ithuta ka mochini li etsa hore ho be bonolo ho lekanya mesebetsi ea ho etsa mohlala, kaha boiketsetso bo netefatsa hore litsebetso lia tsoaroa ebile lia phetoa. Sena se bolela hore lisebelisoa tse ngata tsa data li ka hlahlojoa me mefuta e mengata ea ho bolela esale pele e ka etsoa ka nako e fokolang. 7. Ntlafatso e tsoelang pele: Li-algorithms tsa ho ithuta ka mochine li etselitsoe ho ithuta ho tsoa ho data le ho ntlafatsa tsebetso ea tsona ka nako. Sena se bolela hore mefuta e boletsoeng esale pele e ka ntlafatsoa khafetsa le ho nchafatsoa ho netefatsa ho nepahala ha eona, le ha data ea motheo e fetoha. 