<prompt> Hlalosa mekhoa ea ho thibela ho feta tekano thutong ea mochininan  <response>: Instruction in English: Describe techniques for preventing overfitting in machine learning . Response in English: There are several techniques that can be used to prevent overfitting in machine learning models. Overfitting occurs when a model is trained too well on the training data, resulting in poor performance when the model is applied to new data. Here are some common techniques to address overfitting:1. Cross-validation: This involves partitioning the data into various subsets and then training the model on a subset while testing it on another. By validating the model on different subsets of data, one can ensure that the model is generalizing well and not overfitting.2. Regularization: This technique involves adding a penalty term to the cost function of the model, which discourages large weights and thus prevents the model from fitting the training data too closely.3. Early stopping: This technique involves monitoring the performance of the model on a validation set during training, and stopping the training when the performance on the validation set starts to deteriorate, which indicates that the model is starting to overfit the training data.4. Feature selection: Overfitting can often occur when there are too many input features relative to the number of training examples. By selecting only the most relevant features to include in the model, one can prevent overfitting.5. Model simplification: Using a simpler model with fewer parameters can also help prevent overfitting. For example, using a linear model instead of a complex non-linear model can reduce the likelihood of overfitting.6. Ensembling: Combining the predictions of multiple models, each trained on slightly different subsets of the data, can also help prevent overfitting. The idea behind this approach is that by averaging the predictions of multiple models, the impact of overfitting in any individual model is reduced.By using one or more of these techniques, one can help prevent overfitting and improve the generalization performance of machine learning models. . Response in Sesotho: Ho na le mekhoa e maloa e ka sebelisoang ho thibela ho feta tekano mefuteng ea ho ithuta ea mochini. Overfitting e etsahala ha mohlala o koetlisitsoe hantle haholo ho data ea koetliso, e leng se bakang tsebetso e mpe ha mohlala o sebelisoa ho data e ncha. Mona ke mekhoa e meng e tloaelehileng ea ho sebetsana le ho fetella: 1. Ho netefatsa: Sena se kenyelletsa ho arola lintlha ho li-subsets tse fapaneng ebe joale o koetlisa moetso oa sehlopha ha o ntse o leka ho o mong. Ka ho netefatsa mohlala ho li-subsets tse fapaneng tsa data, motho a ka etsa bonnete ba hore mohlala o akaretsa hantle mme ha o feteletse ho feta tekano. 2. Regularization: Mokhoa ona o kenyelletsa ho eketsa nako ea kotlo mosebetsing oa litšenyehelo oa mohlala, o nyahamisang boima bo boholo me kahoo o thibela mohlala hore o se ke oa lumellana le lintlha tsa koetliso haholo. 3. Ho emisa pele ho nako: Mokhoa ona o kenyelletsa ho shebella tsebetso ea mohlala holima tiiso e behiloeng nakong ea koetliso, le ho emisa koetliso ha tsebetso ea tsebetso ea ho netefatsa e qala ho senyeha, e leng se bontšang hore mohlala o qala ho feta data ea koetliso. 4. Khetho ea likarolo: Hangata ho fetella ho ka etsahala ha ho na le likarolo tse ngata haholo tsa ho kenya tse amanang le palo ea mehlala ea koetliso. Ka ho khetha feela likarolo tse loketseng ka ho fetisisa ho kenyelletsa mohlala, motho a ka thibela ho feta. 5. Ho nolofatsa mohlala: Ho sebelisa mohlala o bonolo o nang le li-parameter tse fokolang ho ka boela ha thusa ho thibela ho feta. Ka mohlala, ho sebelisa mohlala oa linear ho e-na le mokhoa o rarahaneng o sa tsitsang ho ka fokotsa monyetla oa ho feta. 6. Ho kopanya: Ho kopanya likhakanyo tsa mefuta e mengata, e ngoe le e ngoe e koetlisitsoeng ka likaroloana tse fapaneng tsa data, ho ka boela ha thusa ho thibela ho feta. Maikutlo a ka morao ho mokhoa ona ke hore ka ho lekanya likhakanyo tsa mehlala e mengata, phello ea ho feta tekano mofuteng ofe kapa ofe oa motho ka mong e fokotsehile. Ka ho sebelisa e le ngoe kapa tse ngata tsa mekhoa ena, motho a ka thusa ho thibela ho fetella le ho ntlafatsa tsebetso ea kakaretso ea mefuta ea ho ithuta ea mochini. 