<prompt> Ke mekhoa efe e ka sebelisoang ho fumana li-outliers ho dataset?nan  <response>: Instruction in English: What techniques can be used to detect outliers in a dataset? . Response in English: Outliers are unusual observations in a dataset that can potentially skew and mislead the data analysis. Several techniques can be used to detect them in a dataset, including:1. Statistical methods: These methods use standard deviation and interquartile ranges to identify potential outliers. For example, any data point more than 1.5 the interquartile range beyond the lower or upper quartiles are considered outliers. 2. Visual methods: Graphical representations such as box plots, scatter plots, and histograms are useful for detecting outliers. Visual inspection can reveal data points that lie outside the general distribution of the dataset.3. Z-Score: The Z-score represents the number of standard deviations a data point is away from the mean. A high absolute value of Z-score typically greater than 3 can indicate that a data point is an outlier.4. The Modified Z-Score: The modified Z-score uses the median and the median absolute deviation MAD instead of the mean and standard deviation to identify outliers. This method is more robust to outliers, as median is less affected by extreme values than the mean. 5. The Mahalanobis distance: This method calculates the distance of a data point from the mean while accounting for the covariance among the variables. It can identify multivariate outliers in a dataset.6. Machine Learning: Advanced machine learning algorithms, such as isolation forest, local outlier factor, and one-class SVM, can be used to detect outliers, particularly in large and high-dimensional datasets. These algorithms work by learning the general patterns in the dataset and identifying observations that deviate from those patterns.It is essential to note that outliers may occur due to measurement or input errors, or they may be valid observations. Thus, further investigation is necessary before deciding to remove or treat these observations. . Response in Sesotho: Li-outliers ke litebello tse sa tloaelehang ho dataset e ka nang ea khopamisa le ho khelosa tlhahlobo ea data. Mekhoa e mengata e ka sebelisoa ho li lemoha ka hara dataset, ho kenyeletsa: 1. Mekhoa ea lipalo-palo: Mekhoa ena e sebelisa kheloha e tloaelehileng le mekhahlelo ea li-interquartile ho khetholla bao e ka bang ba ka ntle. Ka mohlala, lintlha leha e le life tsa data tse fetang 1.5 interquartile range ho feta li-quartile tse ka tlaase kapa tse ka holimo li nkoa e le tse ka ntle. 2. Mekhoa e bonoang: Litsoantso tsa litšoantšo tse kang lipolane tsa mabokose, libaka tsa ho hasanya, le li-histogram li na le thuso bakeng sa ho lemoha lintho tse kantle. Tlhahlobo ea pono e ka senola lintlha tsa data tse ka ntle ho kabo e akaretsang ea dataset. 3. Z-Score: Lintlha tsa Z li emela palo ea liphapang tse tloaelehileng tseo ntlha ea data e leng hole le moelelo. Boleng bo phahameng ka ho fetesisa ba Z-score hangata bo fetang 3 bo ka bontsa hore ntlha ea data ke kantle. 4. The Modified Z-Score: Z-score e fetotsoeng e sebelisa median le median absolute deviation MAD ho e-na le ho kheloha ho bolelang le ho tloaelehileng ho khetholla tse ka ntle. Mokhoa ona o matla haholo ho barekisi ba kantle, kaha median ha e amehe haholo ke litekanyetso tse feteletseng ho feta moelelo. 5. Bohole ba Mahalanobis: Mokhoa ona o lekanya sebaka sa ntlha ea data ho tloha ho moelelo ha o ntse o bala li-covariance hara mefuta-futa. E ka khetholla li-multivariate outliers ho dataset. 6. Ho Ithuta ka Mochini: Mekhoa e tsoetseng pele ea ho ithuta ka mochine, e kang moru oa ho itšehla thajana, ntho e ka ntle ea sebaka sa heno, le SVM ea sehlopha se le seng, e ka sebelisoa ho lemoha lintho tse ka ntle, haholo-holo ho li-dataset tse kholo le tse phahameng. Litharollo tsena li sebetsa ka ho ithuta mekhoa e akaretsang pokellong ea data le ho tseba maikutlo a fapohang ho mekhoa eo. Ho bohlokoa ho hlokomela hore li-outliers li ka hlaha ka lebaka la liphoso tsa ho lekanya kapa tsa ho kenya, kapa e ka ba litebello tse nepahetseng. Ka hona, ho hlokahala lipatlisiso tse eketsehileng pele u etsa qeto ea ho tlosa kapa ho tšoara litebello tsena. 