<prompt> Hlahisa algorithm ea ho ithuta ka mochini bakeng sa morero o fanoengHo arola mongolo ka lihlopha  <response>: Instruction in English: Generate a machine learning algorithm for the given purpose . Response in English: One of the popular machine learning algorithms that can be used for classifying texts into categories is Naive Bayes Classifier. Below is an example of how it can be implemented using Python programming language:1. Start by loading the necessary libraries:pythonfrom sklearn.feature_extraction.text import CountVectorizerfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.pipeline import Pipelinefrom sklearn.datasets import fetch_20newsgroupsfrom sklearn.metrics import classification_report2. Load the dataset:pythondata fetch_20newsgroupsX data.datay data.target3. Split the dataset into training and testing sets:pythonfrom sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test train_test_splitX, y, random_state424. Create the Naive Bayes classifier:pythonclassifier Pipeline vectorizer, CountVectorizer, classifier, MultinomialNB5. Train the classifier using the training set:pythonclassifier.fitX_train, y_train6. Test the classifier on the testing set to evaluate its performance:pythony_pred classifier.predictX_testprintclassification_reporty_test, y_predThe pipeline combines two steps: first, it converts the text data into numerical features using the CountVectorizer and then it trains a MultinomialNB classifier on the resulting numerical data. This process can be applied to any text classification task by customizing the dataset and target categories according to the specific use case. . Response in Sesotho: ngoe ea li-algorithms tse tsebahalang tsa ho ithuta mochini tse ka sebelisoang ho arola litemana ka mekhahlelo ke Naive Bayes Classifier. Ka tlase ke mohlala oa kamoo e ka kengoang tsebetsong ka puo ea lenaneo la Python: 1. Qala ka ho kenya lilaebrari tse hlokahalang: python from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn .lintlha tsa li-dataset fetch_20newsgroups ho tsoa ho sklearn.metrics import classification_report 2. Laola pokello ea lintlha: python data fetch_20newsgroups X data.data y data.target 3. Arola pokello ea boitsebiso ka koetliso le liteko sets: python from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test train_test_splitX, y, random_state42 4. Theha sehlopha sa Naive Bayes: python classifier Pipeline vectorizer, CountVectorizer, classifier, MultinomialNB 5. Koetlisa mohlophisi u sebelisa sehlopha sa koetliso: python classifier.fitX_train, y_train 6 . Lekola mohlahlobi sehlopheng sa teko ho lekola tshebetso ya sona: python y_pred classifier.predictX_test printclassification_reporty_test, y_pred Pepe e kopanya mehato e meli: ea pele, e fetola data ea mongolo. ka likarolo tsa lipalo ho sebelisa CountVectorizer ebe e koetlisa sehlopha sa MultinomialNB mabapi le lintlha tse hlahisoang ke linomoro. Tsebetso ena e ka sebelisoa mosebetsing ofe kapa ofe oa ho hlophisa mongolo ka ho etsa dathaset le mekhahlelo e reretsoeng ho latela tsebeliso e khethehileng. 