<prompt> Hlalosa mokhoa oa ho ithuta sefate sa liqeto.nan  <response>: Instruction in English: Describe the process of decision tree learning. . Response in English: Decision tree learning is a widely used method for building classification or regression models in machine learning. It involves the recursive partitioning of the data into subsets based on the values of the predictor variables. The algorithm begins with the entire training dataset, which is considered to be the root node. The goal is to determine the best feature to split the data on, based on some measure of purity or information gain. This measure is used to calculate how well the data is split, given the target variable, and helps the algorithm to choose the most important feature for classification at that point.The chosen feature is used to split the data into subsets, creating child nodes. Each of these child nodes is then evaluated and split further, with the process repeating until a stopping criterion is met, such as a certain tree depth is reached or the number of instances in a leaf node is below a certain threshold. At each node, if all instances belong to the same class, then a leaf node is created and labeled with the class name. If not, the algorithm chooses another variable to be split on and the process continues.After the tree is constructed, it can be used to predict new instances by traversing the tree from the root node to a leaf node. At each internal node, the decision is made based on the value of the corresponding feature in the data, and the data is sent to the appropriate child node.One of the main advantages of decision tree learning is its interpretability, as the tree can be easily visualized and the decision-making process is easy to understand. However, the algorithm can be highly sensitive to small changes in the dataset, and care must be taken to avoid overfitting by controlling the depth of the tree or the number of instances in the leaf nodes. . Response in Sesotho: Ho ithuta ka sefate sa liqeto ke mokhoa o sebelisoang haholo bakeng sa ho haha mefuta ea lihlopha kapa tsa ho fokotseha ho ithuteng ka mochine. E kenyelletsa karohano e pheta-phetoang ea data ho li-subsets ho ipapisitsoe le boleng ba mefuta-futa ea selelekela. Algorithm e qala ka dataset eohle ea koetliso, e nkoang e le node ea motso. Sepheo ke ho fumana karolo e ntle ka ho fetisisa eo u ka arolang data ho eona, ho latela tekanyo e itseng ea bohloeki kapa ho fumana boitsebiso. Tekanyo ena e sebelisetsoa ho bala hore na data e arotsoe hantle hakae, ha ho fanoa ka sepheo se fapaneng, me e thusa algorithm ho khetha tšobotsi ea bohlokoa ka ho fetisisa bakeng sa ho arola ka nako eo. Karolo e khethiloeng e sebelisoa ho arola data ka li-subsets, ho theha li-node tsa bana. E ngoe le e ngoe ea li-node tsena tsa bana e ntano hlahlojoa le ho aroloa ho ea pele, ka tsebetso e pheta-pheta ho fihlela tekanyetso ea ho emisa e finyelloa, joalo ka ha botebo bo itseng ba sefate bo fihletsoe kapa palo ea liketsahalo sebakeng sa lekhasi le ka tlasa moeli o itseng. Sebakeng se seng le se seng, haeba maemo ohle e le a sehlopha se le seng, joale node ea lekhasi e thehoa me e ngotsoe ka lebitso la sehlopha. Haeba ho se joalo, algorithm e khetha mofuta o mong o tla aroloa me tsebetso e tsoela pele. Ka mora hore sefate se hahuoe, se ka sebelisoa ho bolela liketsahalo tse ncha ka ho tšela sefate ho tloha motsong oa motso ho ea ho node ea makhasi. Sebakeng se seng le se seng sa ka hare, qeto e etsoa ho latela boleng ba tšobotsi e lumellanang le data, me data e romelloa ho node e loketseng ea bana. E ngoe ea melemo e ka sehloohong ea thuto ea sefate sa liqeto ke tlhaloso ea eona, kaha sefate se ka bonoa habonolo le mokhoa oa ho etsa liqeto o bonolo ho o utloisisa. Leha ho le joalo, algorithm e ka ba e hlokolosi haholo ho liphetoho tse nyenyane tsa dataset, me tlhokomelo e lokela ho nkoa e le ho qoba ho fetella ka ho laola botebo ba sefate kapa palo ea liketsahalo makhasi a makhasi. 