<prompt> Hlalosa hore na mofuta oa ho ithuta mochini o tla tobana le mathata a mofuta ofe.nan  <response>: Instruction in English: Explain what kind of challenges a machine learning model will face. . Response in English: There are several challenges a machine learning model may face. Some of these include:1. Quality of Data: Machine learning algorithms rely heavily on the quality of the data they are trained on. If the data is noisy, incomplete or not representative of the real-world situations, the model will have difficulty in performing accurately and generalizing to new data.2. Complexity: Machine learning models can be complex and difficult to interpret. This can make it challenging to explain how the model is making its predictions and may lead to issues with transparency and trust.3. Overfitting: An overfit model is one that has learned the training data too well, including the noise and errors, and is not able to generalize well to new data. This is a common challenge in machine learning and can be addressed through techniques such as feature selection and regularization.4. Bias: Machine learning models can perpetuate existing biases in the data, leading to discriminatory or unfair outcomes. Bias can arise from a variety of sources, such as the way the data is collected or the way the model is trained. Mitigating bias is an active area of research in the field.5. Computation: Training machine learning models can be computationally intensive and require significant amounts of computing power and memory, especially when working with large datasets. This can be a challenge when trying to scale machine learning models and deploy them in production.6. Hyperparameter Tuning: Machine learning models often have many hyperparameters that need to be carefully tuned in order to achieve the best performance. This can be a time-consuming and complex process and requires a deep understanding of the underlying algorithms.7. Change in Data Distribution: Machine learning models are typically trained on a specific distribution of data, but this distribution can change over time, leading to a decrease in model performance. This challenge, known as concept drift or data drift, requires continuous monitoring and model re-training to ensure the model remains accurate. . Response in Sesotho: Ho na le mathata a maloa ao mofuta oa ho ithuta oa mochini o ka tobanang le ona. Tse ling tsa tsona li kenyelletsa: 1. Boleng ba Boitsebiso: Mekhoa ea ho ithuta ka mochine e itšetlehile haholo ka boleng ba lintlha tseo ba li koetlisitsoeng. Haeba data e le lerata, e sa phethoa kapa e se moemeli oa maemo a sebele a lefatse, mohlala o tla ba le bothata ba ho sebetsa ka nepo le ka kakaretso ho data e ncha. 2. Ho rarahana: Mehlala ea ho ithuta ka mochini e ka ba thata ebile e thata ho e hlalosa. Sena se ka etsa hore ho be thata ho hlalosa hore na mohlala o etsa likhakanyo tsa ona joang mme ho ka lebisa mathateng a pepeneneng le ka tsepo. 3. Overfitting: Mohlala oa overfit ke o ithutileng lintlha tsa koetliso hantle haholo, ho kenyeletsa lerata le liphoso, me ha o khone ho akaretsa hantle ho data e ncha. Ena ke phephetso e atileng thutong ea mochini me e ka rarolloa ka mekhoa e joalo ka khetho ea likarolo le ho e etsa kamehla. 4. Leeme: Mehlala ea ho ithuta ka mochine e ka ntšetsa pele leeme le teng ho data, e lebisang liphellong tse khethollang kapa tse sa lokang. Leeme le ka hlaha mehloling e fapaneng, joalo ka tsela eo data e bokelloang ka eona kapa tsela eo mohlala o koetliselitsoeng ka eona. Ho fokotsa leeme ke sebaka se sebetsang sa lipatlisiso tšimong. 5. Compution: Mehlala ea ho ithuta ka mochini e ka ba matla haholo me ea hloka matla a mangata a komporo le mohopolo, haholo ha o sebetsa ka li-dataset tse kholo. Sena e ka ba phephetso ha u leka ho lekanya mefuta ea ho ithuta ea mochini le ho e sebelisa tlhahisong. 6. Hyperparameter Tuning: Mehlala ea ho ithuta mochine hangata e na le li-hyperparameter tse ngata tse hlokang ho hlophisoa ka hloko e le hore li finyelle tsebetso e ntle ka ho fetisisa. Sena e ka ba tsebetso e jang nako le e rarahaneng me e hloka kutloisiso e tebileng ea li-algorithms tsa motheo. 7. Phetoho ka Phatlalatso ea Boitsebiso: Mehlala ea ho ithuta ka mochine e atisa ho koetlisetsoa kabong e itseng ea data, empa kabo ena e ka fetoha ha nako e ntse e ea, e lebisang ho fokotseha ha tsebetso ea mohlala. Phephetso ena, e tsejoang ka hore ke concept drift kapa data drift, e hloka tlhahlobo e tsoelang pele le boithuto bo bocha ho netefatsa hore mohlala o lula o nepahetse. 