<prompt> Ngola mohlala oa lihlopha ho fumana hore na tlhahiso e boletsoeng esale pele ea tatellano e fanoeng ea linomoro e lekana kapa e sa tloaeleha.2, 4, 6, 8  <response>: Instruction in English: Write a classification model to determine if the predicted output of a given sequence of digits is even or odd. . Response in English: Here is an example code of a classification model that can be used to determine if the predicted output of a given sequence of digits is even or odd. This model is written in Python and uses the NumPy and scikit-learn libraries:pythonimport numpy as npfrom sklearn.naive_bayes import GaussianNB Define a function to convert a list of digits to a 2D array where each row is a digitdef digits_to_arraydigits: result for digit in digits: row 0 10 rowdigit 1 result.appendrow return np.arrayresult Define a function to train the modeldef train_modelX, y: classifier GaussianNB classifier.fitX, y return classifier Define a function to predict the outputdef predict_outputclassifier, digits: X digits_to_arraydigits y_pred classifier.predictX return y_pred Define the training dataX_train digits_to_array2, 4, 6, 8y_train np.array0, 0, 0, 0 0 for even and 1 for odd Train the modelclassifier train_modelX_train, y_train Test the modeldigits 1, 3, 5, 7output predict_outputclassifier, digitsprintoutput Expected output: 1 1 1Note that this model uses a Naive Bayes classifier trained on a small dataset. In practice, you would need to train the model on a larger and more varied dataset to ensure that it generalizes well to different inputs. You could also experiment with different classification algorithms to see if they improve the accuracy of the model. . Response in Sesotho: Mona ke mohlala oa mohlala oa mokhoa oa ho khetholla o ka sebelisoang ho fumana hore na tlhahiso e boletsoeng esale pele ea tatellano e fanoeng ea linomoro e lekana kapa e sa tloaeleha. Moetso ona o ngotsoe ka Python me o sebelisa lilaeborari tsa NumPy le scikit-lear: python import numpy as np from sklearn.naive_bayes import GaussianNB Hlalosa tšebetso ho fetolela lenane la linomoro ho sehlopha sa 2D moo mola o mong le o mong e leng linomoro. def digits_to_arraydigits: result bakeng sa palo ka linomoro: mola 0 10 rowdigit 1 sephetho.appendrow kgutlisetsa np.arraysephetho Hlalosa mosebetsi ho koetlisa moetso def train_modelX, y: classifier GaussianNB classifier.fitX, y return classifier Hlalosa tsebetso ho bolela esale pele tlhahiso ea def predict_outputsehlopha, linomoro: X linomoro_to_arraylinomoro y_pred classifier.predict X khutla y_pred Hlalosa lintlha tsa koetliso X_train linomoro_to_array2, 4, 6, 8 y_train np.array0, 0, 0, 0 0 bakeng sa esita le 1 bakeng sa odd Koetlisa model classifier train_modelX_train, y_train Leka dinomoro tsa mohlala 1, 3, 5, 7 output predict_outputclassifier, digit printoutput Sephetho se lebelletsoeng: 1 1 1 Tlhokomeliso hore mofuta ona o sebelisa sehlopha sa Naive Bayes se koetliselitsoeng ho dataset e nyane. Ha e le hantle, o tla hloka ho koetlisa mohlala ho dataset e kholoanyane le e fapaneng ho feta ho netefatsa hore e sebetsa hantle ho tse fapaneng. U ka boela ua etsa liteko ka li-algorithms tse fapaneng ho bona hore na li ntlafatsa ho nepahala ha mohlala. 