<prompt> Hlalosa tsebetso ea Naive Bayes Classification.nan  <response>: Instruction in English: Explain the process of Naive Bayes Classification. . Response in English: Naive Bayes is a probabilistic classification algorithm based on Bayes theorem with the assumption of independence among the features. It is called naive because it assumes that each input variable is independent of all the other variables, which may not always be the case in real-life situations.Here are the basic steps involved in the process of Naive Bayes Classification:1. Identify the features: Choose the relevant features used to represent the data or the problem being solved. Features can be, for example, the individual words of an email or characteristics of a medical patient.2. Calculate the class priors: These are the probabilities of encountering each class, computed by dividing the number of occurrences of a given class by the total number of samples. If we have a dataset of emails, where we are interested in classifying them as spam or not spam, we would calculate the probability of an email being spam and the probability of an email not being spam.3. Calculate the likelihood: This is the probability of the occurrence of each feature given the class. We would calculate the probability of each word feature for each class. For example, the probability of a given word appearing in a spam email or in a non-spam email.4. Make the prediction: For a new data point, apply Bayes theorem to calculate the posterior probability of each class given the features of the data point. The formula for Bayes theorem is: PAB PBA PA PB. The class with the highest posterior probability is the predicted class for the data point.5. Update the model: As new data is obtained, the model can be updated by recalculating the class priors and the likelihoods. This allows the model to incrementally learn from new data.Naive Bayes is a simple and fast classification algorithm that performs well, particularly with high-dimensional data such as text classification. Its simplicity makes it easy to interpret and understand, and its assumption of independence among features makes it well-suited for working with large datasets. . Response in Sesotho: Naive Bayes ke probabilistic classification algorithm e thehiloeng ho theorem ea Bayes ka maikutlo a boipuso hara likarolo. E bitsoa naive hobane e nka hore mofuta o mong le o mong oa ho kenya o itšetlehile ka mefuta e meng kaofela, e ka nang ea se ke ea e-ba joalo kamehla maemong a sebele a bophelo. Mona ke mehato ea mantlha e amehang tšebetsong ea Naive Bayes Classification: 1. Khetholla likarolo: Khetha likarolo tse loketseng tse sebelisoang ho emela data kapa bothata bo rarolloang. Likarolo e ka ba, mohlala, mantsoe a motho ka mong a lengolo-tsoibila kapa litšobotsi tsa mokuli oa bongaka. 2. Bala lintlha tsa pele tsa sehlopha: Tsena ke menyetla ea ho kopana le sehlopha ka seng, se bakiloeng ka ho arola palo ea liketsahalo tsa sehlopha se itseng ka kakaretso ea lisampole. Haeba re na le pokello ea data ea li-imeile, moo re ratang ho li khetholla e le spam kapa e se spam, re tla lekanya monyetla oa hore lengolo-tsoibila e be spam le monyetla oa hore lengolo-tsoibila e se spam. 3. Bala monyetla: Ona ke monyetla oa ho hlaha ha karolo ka ngoe e fanoeng ke sehlopha. Re ne re tla bala monyetla oa lentsoe ka leng tšobotsi bakeng sa sehlopha ka seng. Mohlala, monyetla oa hore lentsoe le fanoeng le hlahang lengolo-tsoibila la spam kapa lengolo-tsoibila leo e seng spam. 4. Etsa se boletsoeng esale pele: Bakeng sa lintlha tse ncha, sebelisa theorem ea Bayes ho bala monyetla oa morao-rao oa sehlopha ka seng ho latela likarolo tsa ntlha ea data. Foromo ea theorem ea Bayes ke: PAB PBA PA PB. Sehlopha se nang le menyetla e phahameng ka ho fetisisa ea morao-rao ke sehlopha se boletsoeng esale pele bakeng sa ntlha ea data. 5. Ntlafatsa mohlala: Ha data e ncha e fumanoa, mohlala o ka ntlafatsoa ka ho bala hape lintlha tsa pele tsa sehlopha le menyetla. Sena se lumella mohlala ho ithuta haholoanyane ho tsoa ho data e ncha. Naive Bayes ke algorithm e bonolo le e potlakileng ea ho hlophisa e sebetsang hantle, haholo ka data ea maemo a holimo joalo ka ho hlopha mongolo. Ho ba bonolo ha eona ho etsa hore ho be bonolo ho e hlalosa le ho e utloisisa, me maikutlo a eona a ho ikemela hara likarolo a etsa hore e tšoanelehe bakeng sa ho sebetsa le li-dataset tse kholo. 