<prompt> Ke mathata afe ao ho tobanoang le ona ha ho koetlisoa mohlala oa ho ithuta mochine?nan  <response>: Instruction in English: What challenges are faced when training a machine learning model? . Response in English: Training a machine learning model is a complex process that involves multiple steps and challenges. Some of the most common challenges faced during this process include:1. Data Quality: One of the most significant challenges when it comes to training a machine learning model is ensuring that the data used is of high quality. Noisy and inconsistent data can significantly affect the accuracy of the model.2. Data Preprocessing: Before training a machine learning model, data must be preprocessed to ensure that it is in the appropriate format for the algorithm. This includes tasks such as cleaning, normalizing, and transforming the data.3. Feature Selection: Choosing the right features is crucial when training a machine learning model. Including irrelevant or redundant features can lead to decreased accuracy and increased computation time.4. Model Selection: Selecting the best model for a given dataset can be a challenge. There are several models available, each with its strengths and weaknesses.5. Overfitting: Overfitting is a common problem when training a machine learning model. It happens when the model becomes so complex that it fits the training data perfectly, but performs poorly on new data.6. Computation Time: Training a machine learning model can take a long time, especially when working with large datasets or complex models.7. Hyperparameter Tuning: The performance of a machine learning model can be significantly improved by tuning its hyperparameters. However, finding the best set of hyperparameters can be time-consuming and computationally expensive.In summary, training a machine learning model involves numerous challenges, ranging from data preprocessing and feature selection to model selection and hyperparameter tuning. Overcoming these challenges is critical to achieving high accuracy and robustness in a machine learning model. . Response in Sesotho: Ho koetlisa mokhoa oa ho ithuta mochini ke tsebetso e rarahaneng e kenyelletsang mehato le liphephetso tse ngata. Tse ling tsa liphephetso tse tloaelehileng tse tobaneng le tsebetso ena li kenyelletsa: 1. Boleng ba Boitsebiso: E ngoe ea mathata a bohlokoa ka ho fetisisa ha ho tluoa tabeng ea ho koetlisa mokhoa oa ho ithuta mochine ke ho netefatsa hore lintlha tse sebelisoang ke tsa boleng bo phahameng. Lintlha tse lerata le tse sa tsitsang li ka ama haholo ho nepahala ha mohlala. 2. Phatlalatso ea Boitsebiso: Pele o koetlisa mohlala oa ho ithuta mochine, lintlha li tlameha ho sebetsoa esale pele ho netefatsa hore li ka mokhoa o nepahetseng bakeng sa algorithm. Sena se kenyelletsa mesebetsi e kang ho hloekisa, ho tloaeleha, le ho fetola data. 3. Khetho ea Likarolo: Ho bohlokoa ho khetha likarolo tse nepahetseng ha u koetlisa mohlala oa ho ithuta ka mochini. Ho kenyelletsa likarolo tse sa hlokahaleng kapa tse sa hlokahaleng ho ka lebisa ho fokotseng ho nepahala le ho eketseha ha nako ea ho bala. 4. Khetho ea Mohlala: Ho khetha mohlala o motle ka ho fetisisa bakeng sa dataset e fanoeng e ka ba phephetso. Ho na le mehlala e mengata e fumanehang, e ngoe le e ngoe e na le matla le mefokolo ea eona. 5. Overfitting: Overfitting ke bothata bo tloaelehileng ha u koetlisa mohlala oa ho ithuta mochine. Ho etsahala ha mohlala o fetoha o rarahaneng hoo o lumellanang le data ea koetliso ka mokhoa o phethahetseng, empa o sebetsa hampe ho data e ncha. 6. Nako ea Kopo: Ho koetlisa mohlala oa ho ithuta mochine ho ka nka nako e telele, haholo-holo ha ho sebetsa ka li-dataset tse kholo kapa mehlala e rarahaneng. 7. Hyperparameter Tuning: Tsebetso ea mohlala oa ho ithuta mochine e ka ntlafatsoa haholo ka ho lokisa li-hyperparameter tsa eona. Leha ho le joalo, ho fumana li-hyperparameter tse ntle ka ho fetisisa ho ka nka nako ebile ho bitsa chelete e ngata. Ka bokhutšoanyane, ho koetlisa mohlala oa ho ithuta mochini ho kenyelletsa mathata a mangata, ho tloha ho tsebetso ea data esale pele le khetho ea likarolo ho ea ho khetho ea mohlala le hyperparameter tuning. Ho hlola liphephetso tsena ho bohlokoa ho fihlela ho nepahala le ho tiea ho holimo mokhoeng oa ho ithuta oa mochini. 