<prompt> Hlahisa sehlopha sa sehlopha se bolelang esale pele hore na ntlo e fanoeng e theko e boima kapa che ho latela likarolo tsa ho kenya.palo ea likamore tsa ho robala palo ea likamore tsa ho hlapa setšoantšo sa lisekoere 3 2 2,000 3 2 4,000 4 3 2,500 4 4 5,500  <response>: Instruction in English: Generate a classifier that accurately predicts whether a given house is expensive or not based on input features. . Response in English: To generate a classifier, we need to know the classification labels to predict - here, whether a house is expensive or not. To do this, we need training data with labels indicating which houses in the sample dataset are expensive and which are not. Once we have that, we can train a classifier using various machine learning algorithms such as logistic regression, decision tree, or support vector machine. The input data should be preprocessed before being fed into the classifier. The preprocessing steps may include converting categorical data into numerical values, scaling or normalizing the numerical data, handling missing values, among other steps. For instance, lets suppose we have training labels indicating that the first two houses in the input data are not expensive, and the last two houses are expensive. Below is a possible implementation of a classifier using the logistic regression algorithm in Python with the scikit-learn library:from sklearn.linear_model import LogisticRegressioninput dataX 3, 2, 2000, 3, 2, 4000, 4, 3, 2500, 4, 4, 5500classification labels 0 for not expensive, 1 for expensivey 0, 0, 1, 1fitting the modelclf LogisticRegressionsolverliblinear.fitX, y making predictions using the fitted modelpredictions clf.predictXAfter training the classification model on the input data, it can be used to predict whether new houses are expensive or not based on the number of bedrooms, bathrooms, and square footage. This is just one possible implementation, and the accuracy of the classifier will depend on the quality and quantity of the training data available. . Response in Sesotho: Ho hlahisa classifier, re hloka ho tseba li-labels tsa lihlopha ho bolela esale pele - mona, hore na ntlo e theko e boima kapa che. Ho etsa sena, re hloka lintlha tsa koetliso tse nang le lileibole tse bontšang hore na ke matlo afe ka hara sephutheloana sa data a bitsang chelete e ngata le a sa ture. Ha re se re e-na le seo, re ka koetlisa mohlophisi ka ho sebelisa li-algorithms tse fapaneng tsa ho ithuta tsa mochini joalo ka ho fokotseha ha lintho, sefate sa liqeto, kapa mochini o tšehetsang vector. Lintlha tse kentsoeng li lokela ho sebetsoa esale pele pele li kenngoa ho sehlopha. Mehato ea pele ho tsebetso e ka kenyelletsa ho fetolela data ea likarolo hore e be lipalo tsa linomoro, ho lekanya kapa ho tloaeleha ha lintlha tsa linomoro, ho sebetsana le litekanyetso tse sieo, hara mehato e meng. Mohlala, ha re nke hore re na le lileibole tsa koetliso tse bontšang hore matlo a mabeli a pele a data ea ho kenya ha a turu, me matlo a mabeli a ho qetela a theko e boima. Ka tlase ho na le tsebetsong e ka bang teng ea motho ea khethollang ho sebelisa algorithm ea ho khutlisetsa lintho ho Python ka laeborari ea ho ithuta ka scikit: ho tsoa ho sklearn.linear_model import LogisticRegression input data X 3, 2, 2000, 3, 2, 4000, 4, 3, 2500, 4, 4, 5500 classification labels 0 bakeng sa ha e theko e boima, 1 e theko e boima y 0, 0, 1, 1 fitting the model clf LogisticRegression solverliblinear.fitX, y ho bolela esale pele ka ho sebelisa mekhoa e boletsoeng esale pele ea mohlala clf.predictX Ka mora ho koetlisa moetso oa lihlopha ho data ea ho kenya, e ka sebelisoa ho bolela esale pele hore na e ncha. matlo a theko e boima kapa che, ho ipapisitsoe le palo ea likamore tsa ho robala, likamore tsa ho hlapela, le litšoantšo tsa lisekoere. Ena ke tsebetsong e le ngoe feela e ka khonehang, me ho nepahala ha classifier ho tla itšetleha ka boleng le bongata ba lintlha tsa koetliso tse teng. 