<prompt> Hlalosa mohopolo oa moru o sa reroang.nan  <response>: Instruction in English: Explain the concept of random forest. . Response in English: A random forest is a type of machine learning algorithm based on the idea of ensemble learning, which refers to the process of generating many classifiers or decision trees and then merging their outputs to improve the accuracy and robustness of prediction.A random forest consists of numerous decision treeseach tree represents different sets of rules for making a prediction, and the target class prediction is based on a majority vote across all of the trees in the forest. This approach reduces overfitting, which is when a model is tailored too closely to the training data, resulting in poor performance on new, unseen data.The decision trees in a random forest are trained using a process called bootstrapping. This means that the algorithm creates sub-samples of the training data by randomly selecting data points with replacement. Each tree is grown using a different bootstrap dataset, so each tree is slightly different from the others.Additionally, during the tree building process, only a random subset of the features is considered for splitting a node, introducing further randomness in the model. This randomness helps to increase the diversity of the trees and, ultimately, makes the model more powerful.Random forests are widely used in various applications such as regression, classification, and feature selection, and have become a popular and powerful tool in the field of machine learning. . Response in Sesotho: Moru o sa reroang ke mofuta oa algorithm ea ho ithuta mochini o ipapisitseng le mohopolo oa ho kopanya thuto, e bolelang tsebetso ea ho hlahisa lihlopha tse ngata kapa lifate tsa liqeto ebe o kopanya liphetho tsa bona ho ntlafatsa ho nepahala le ho tiea ha ponelopele. Moru o sa reroang o na le lifate tse ngata tsa liqeto-sefate se seng le se seng se emela lihlopha tse fapaneng tsa melao ea ho bolela esale pele, me ponelopele ea sehlopha se reretsoeng e ipapisitse le likhetho tse ngata holima lifate tsohle tse morung. Mokhoa ona o fokotsa ho feta, e leng ha mohlala o lokiselitsoe haufi-ufi le data ea koetliso, e leng se fellang ka ho se sebetse hantle ho data e ncha, e sa bonahaleng. Lifate tsa liqeto morung o sa reroang li koetlisetsoa ho sebelisa mokhoa o bitsoang bootstrapping. Sena se bolela hore algorithm e theha lisampole tse nyane tsa data ea koetliso ka ho khetha lintlha tsa data ka mokhoa o mong. Sefate se seng le se seng se holisoa ho sebelisoa dataset e fapaneng ea bootstrap, kahoo sefate ka seng se fapane hanyane le tse ling. Ho phaella moo, nakong ea tsebetso ea ho haha sefate, ho na le karolo e nyenyane feela ea likarolo tse nkiloeng bakeng sa ho arola node, ho hlahisa mokhoa o mong o sa tloaelehang oa mohlala. Sena se sa tloaelehang se thusa ho eketsa mefuta-futa ea lifate me, qetellong, e etsa hore mohlala o be matla haholoanyane. Meru e sa tloaelehang e sebelisoa haholo litsebetsong tse fapaneng tse kang ho fokotseha, ho hlophisoa, le khetho ea likarolo, me e fetohile sesebelisoa se tummeng le se matla tšimong ea ho ithuta mochine. 