<prompt> Melemo ea sefate sa liqeto ke efe?nan  <response>: Instruction in English: What are the advantages of a decision tree? . Response in English: A decision tree is a predictive model that can be used for classification or regression problems. It involves creating a tree-like structure where internal nodes represent tests on attributes, branches represent the outcome of a test, and the leaf nodes represent class labels or a numerical value. Some of the advantages of using decision trees include:1. Easy to Understand and Interpret: Decision trees are visual representations of a problem, which makes it easier to understand and follow the decision-making process. The tree can be easily interpreted by humans, so even non-data analysts can understand the significance of a decision tree.2. Handles both Categorical and Numerical Data: Decision trees can work with both categorical and numerical data, which makes them useful for a variety of problems.3. Non-parametric: Decision trees do not make any assumptions about the underlying data distribution, unlike many other methods that make simplifying assumptions about the data, and are parametric models. This makes them a popular choice when there is little knowledge about the relationships in the data.4. Requires Little Data Preparation: Decision tree algorithms require relatively little data preparation compared to other methods like neural networks. For example, data scaling or normalization is not necessary for decision trees.5. Good for Exploratory Analysis: Decision trees are good for exploratory analysis because they can quickly identify significant variables and their interactions in the data.6. Can Handle Missing Values: Most decision tree algorithms can handle missing data gracefully by either ignoring them or inferring reasonable values where data is missing.7. Can Identify Feature Importance: Based on the structure of the tree, it is possible to measure the importance of each feature, in terms of its ability to partition the data and reduce impurity.8. Simplicity and Speed: Decision trees are relatively simple to understand and implement, and they are also generally fast to build and evaluate, even with larger datasets. . Response in Sesotho: Sefate sa liqeto ke mohlala oa ho bolela esale pele o ka sebelisoang bakeng sa mathata a lihlopha kapa a ho khutlela morao. E kenyelletsa ho theha sebopeho se kang sefate moo li-node tsa ka hare li emelang liteko tsa litšoaneleho, makala a emela sephetho sa tlhahlobo, me li-node tsa makhasi li emela lileibole tsa lihlopha kapa boleng ba linomoro. Melemo e meng ea ho sebelisa lifate tsa liqeto e kenyeletsa: 1. Ho Bonolo ho Utloisisa le ho Toloka: Lifate tsa liqeto ke lipontšo tse bonahalang tsa bothata, e leng se etsang hore ho be bonolo ho utloisisa le ho latela mokhoa oa ho etsa liqeto. Sefate se ka hlalosoa habonolo ke batho, kahoo esita le bahlahlobisisi bao e seng ba data ba ka utloisisa bohlokoa ba sefate sa qeto. 2. E sebetsana le Lintlha tse peli tsa Categorical le Numerical: Lifate tsa liqeto li ka sebetsa ka lintlha tse peli tsa lihlopha le tsa linomoro, e leng se etsang hore li be molemo bakeng sa mathata a sa tšoaneng. 3. Non-parametric: Lifate tsa liqeto ha li etse maikutlo leha e le afe mabapi le kabo ea data ea motheo, ho fapana le mekhoa e meng e mengata e etsang hore ho be bonolo ho nahana ka lintlha, me ke mehlala ea parametric. Sena se ba etsa khetho e tsebahalang ha ho na le tsebo e nyane mabapi le likamano tse data. 4. E Hloka Boitokisetso ba Boitsebiso bo Nyenyane: Li-algorithms tsa sefate sa liqeto li hloka ho lokisoa ha data ha ho bapisoa le mekhoa e meng e kang neural network. Ka mohlala, ho eketsa lintlha kapa ho tloaeleha ha ho hlokahale bakeng sa lifate tsa liqeto. 5. Molemo bakeng sa Tlhahlobo ea Tlhahlobo: Lifate tsa liqeto li molemo bakeng sa tlhahlobo ea lipatlisiso hobane li ka tseba ka potlako mefuta-futa ea bohlokoa le ho sebelisana ha tsona ho data. 6. E ka sebetsana le Melao-motheo e Sieo: Li-algorithms tse ngata tsa sefate sa liqeto li ka sebetsana le data e sieo ka mokhoa o motle ka ho li hlokomoloha kapa ho fana ka litekanyetso tse utloahalang moo data e leng sieo. 7. E ka Khetholla Bohlokoa ba Tšobotsi: Ho itšetlehile ka sebopeho sa sefate, hoa khoneha ho lekanya bohlokoa ba tšobotsi e ngoe le e ngoe, ho latela bokhoni ba eona ba ho arola lintlha le ho fokotsa litšila. 8. Bonolo le Lebelo: Lifate tsa liqeto li batla li le bonolo ho li utloisisa le ho li sebelisa, me ka kakaretso li potlakile ho haha le ho li hlahloba, esita le ka li-dataset tse kholoanyane. 