<prompt> Theha mokhoa oa ho hlophisa lintho ho latela boholo ba tsona tse nyane, tse mahareng le tse kholonan  <response>: Instruction in English: Design a system to classify objects based on its size small, medium and large . Response in English: The system for classifying objects based on size can be developed using the following steps: Step 1: Data CollectionCollect images or measurements of various objects with known sizes. This data will be used to train our model for classification. We need to make sure that we have a balanced dataset with a good representation of small, medium, and large objects. Step 2: Feature ExtractionDevelop an algorithm to extract relevant features of the objects, such as their dimensions or pixel count. These features should be able to differentiate the objects in terms of size. The feature extraction algorithm can be fine-tuned based on the specific requirements of the classification task. Step 3: Model TrainingOnce we have extracted the features from the objects, we can use this data to train a supervised machine learning model. Some of the models which can be used for this classification task are Decision Trees, Random Forests, Support Vector Machines or Neural Networks. In the training process, the model learns the relationship between features and the size of objects. Step 4: Setting ThresholdsAfter the model has been trained, we need to identify the thresholds for the classification of the objects. For instance, we need to define minimum and maximum values for small, medium, and large objects based on their features. Step 5: Testing and DeploymentAfter the model has been trained, we can use it to classify new objects based on their features. The model can be tested on a hold-out dataset or using cross-validation to ensure that it is accurately classifying the objects. It is also essential to evaluate the models performance over time and make any necessary updates to ensure its accuracy.In conclusion, the above system can be utilized to classify objects based on their size, and it involves the steps of data collection, feature extraction, model training, setting thresholds, and testing and deployment. . Response in Sesotho: Mokhoa oa ho arola lintho ho latela boholo o ka ntlafatsoa ho sebelisoa mehato e latelang: Mohato oa 1: Pokello ea Boitsebiso Bokella litšoantšo kapa litekanyo tsa lintho tse fapa-fapaneng ka boholo bo tsejoang. Lintlha tsena li tla sebelisoa ho koetlisa mohlala oa rona bakeng sa ho arola. Re hloka ho etsa bonnete ba hore re na le dataset e leka-lekaneng e nang le kemelo e ntle ea lintho tse nyane, tse mahareng le tse kholo. Mohato oa 2: Feature Extraction Theha algorithm ho ntša likarolo tse amehang tsa lintho, joalo ka boholo ba tsona kapa palo ea pixel. Likarolo tsena li lokela ho tseba ho khetholla lintho ka boholo. Algorithm ea tsebetso ea tsebetso e ka hlophisoa hantle ho latela litlhoko tse ikhethileng tsa mosebetsi oa ho hlophisa. Mohato oa 3: Koetliso ea Mohlala Ha re se re ntšitse likarolo ho tsoa linthong, re ka sebelisa lintlha tsena ho koetlisa mohlala oa ho ithuta oa mochini o hlokometsoeng. E meng ea mefuta e ka sebelisoang bakeng sa mosebetsi ona oa ho hlophisa ke Lifate tsa Liqeto, Meru e sa Lebelloang, Mechini ea Vector ea Tšehetso kapa Neural Networks. Tsebetsong ea koetliso, mohlala o ithuta kamano pakeng tsa likarolo le boholo ba lintho. Mohato oa 4: Ho Bea Mehaho Ka mora hore mohlala o rupeloe, re lokela ho hloaea menyako ea ho arola lintho. Mohlala, re hloka ho hlalosa boleng ba bonyane le boholo ba lintho tse nyane, tse mahareng le tse kholo ho latela likarolo tsa tsona. Mohato oa 5: Teko le Tsebetso Kamora hore mohlala o koetlisoe, re ka o sebelisa ho hlophisa lintho tse ncha ho latela likarolo tsa tsona. Moetso ona o ka lekoa ho dataset ea ho tsoara kapa ho sebelisoa netefatso ea ho netefatsa hore o hlophisa lintho ka nepo. Ho bohlokoa hape ho lekola tsebetso ea mohlala ha nako e ntse e ea le ho etsa lintlafatso tse hlokahalang ho netefatsa ho nepahala ha eona. Qetellong, tsamaiso e ka holimo e ka sebelisoa ho arola lintho ho latela boholo ba tsona, me e kenyelletsa mehato ea ho bokella lintlha, ho ntša likarolo, koetliso ea mohlala, ho beha menyako, le tlhahlobo le ho tsamaisoa. 