<prompt> Hlalosa likarolo tse fapaneng tsa sefate sa liqeto.nan  <response>: Instruction in English: Describe the various components of a decision tree. . Response in English: A decision tree is a widely used machine learning algorithm for decision-making, classification, and prediction. It is a flowchart-like structure that involves several components, which include the following:1. Nodes: A node represents a feature or attribute that splits the data into two or more branches based on some condition. There are three types of nodes in a decision tree: - Root node: Represents the entire dataset or population and is the starting point of the decision tree. It is used to split the data into two or more branches based on the most significant feature. - Internal node: Represents a decision point and is used to split data further into branches based on some condition. - Leaf node: Represents the final decision or outcome of a decision tree. It contains the class label or prediction for a particular instance.2. Branch or Edge: A branch represents a possible outcome or path, created by splitting the data based on the nodes condition. It connects the nodes of a decision tree and represents a decision rule.3. Splitting: Splitting is the process of dividing the dataset into smaller subsets based on the selected feature. It is done at each node of the tree and aims to create branches with the most homogeneous or similar instances.4. Decision Rule: A decision rule is a condition applied to the data at each node of the decision tree. It is used to split the data into branches based on the outcome of the condition.5. Pruning: Pruning is the process of removing branches from a decision tree to avoid overfitting. It is used to simplify a decision tree by removing the least important branches.6. Criteria: Criteria are the measures used to determine the best split at each node of a decision tree. Some commonly used criteria are Gini impurity, information gain, and gain ratio.Overall, a decision tree is built by selecting the best feature based on the chosen criteria and splitting the data into branches, followed by recursively applying the same process until a stopping condition is reached, such as reaching the maximum depth or minimum leaf size. . Response in Sesotho: Sefate sa liqeto ke mokhoa o sebelisoang haholo oa ho ithuta mochini bakeng sa ho etsa liqeto, ho hlophisa le ho bolela esale pele. Ke sebopeho se kang flowchart se kenyelletsang likarolo tse maloa, tse kenyelletsang tse latelang: 1. Node: Node e emela tšobotsi kapa tšobotsi e arolang boitsebiso ka makala a mabeli kapa ho feta ho latela boemo bo itseng. Ho na le mefuta e meraro ea li-node sefateng sa liqeto: - Node ea Motso: E emela dataset eohle kapa palo ea batho me ke sebaka sa ho qala sefate sa qeto. E sebelisoa ho arola data ka makala a mabeli kapa ho feta ho latela tšobotsi ea bohlokoahali. - Node ea ka hare: E emela ntlha ea liqeto me e sebelisoa ho arola lintlha ho ea ho makala ho latela maemo a itseng. - Node ea lekhasi: E emela qeto ea ho qetela kapa sephetho sa sefate sa qeto. E na le leibole ea sehlopha kapa khakanyo ea ketsahalo e itseng. 2. Lekala kapa Edge: Lekala le emela sephetho se ka khonehang kapa tsela, e entsoeng ka ho arola data ho latela boemo ba node. E kopanya li-node tsa sefate sa qeto me e emela molao oa liqeto. 3. Ho arola: Ho arola ke mokhoa oa ho arola dataset ka likaroloana tse nyenyane ho latela tšobotsi e khethiloeng. E etsoa sebakeng se seng le se seng sa sefate me se ikemiselitse ho theha makala a nang le maemo a homogeneous kapa a tšoanang. 4. Molao oa Qeto: Molao oa qeto ke boemo bo sebelisoang ho data sebakeng se seng le se seng sa sefate sa qeto. E sebelisoa ho arola lintlha ka makala ho latela sephetho sa boemo. 5. Ho faola limela: Ho faola limela ke mokhoa oa ho tlosa makala sefateng sa liqeto ho qoba ho feta tekano. E sebelisetsoa ho nolofatsa sefate sa liqeto ka ho tlosa makala a bohlokoa haholo. 6. Litekanyetso: Litekanyetso ke mehato e sebelisetsoang ho fumana hore na ho na le phapang e ntle ka ho fetisisa sebakeng se seng le se seng sa sefate sa liqeto. Mekhoa e meng e sebelisoang hangata ke ho se hloeke ha Gini, phaello ea tlhahisoleseling, le karolelano ea phaello. Ka kakaretso, sefate sa liqeto se hahoa ka ho khetha tšobotsi e ntle ka ho fetisisa ho latela litekanyetso tse khethiloeng le ho arola boitsebiso ka makala, ho lateloa ke ho sebelisa mokhoa o tsoanang ka ho pheta-pheta ho fihlela boemo ba ho emisa bo fihla, joalo ka ho fihla botebong ba botebo kapa boholo ba makhasi a fokolang. 