Question 1

Figure from the original question paper Given below a y_train list which consists of pizza’s ordered by the customers in a shop. y_train = [['regular', 'veg'], ['medium', 'veg'], ['regular', 'non-veg'], ['medium', 'non-veg']] MultiLabelBinarizer from sklearn library has been used to convert the y_train into numbers, so which of the following option matches with the output using the following code ? from sklearn.preprocessing import MultiLabelBinarizer mlb = MultiLabelBinarizer(classes=['regular','medium', 'veg', 'non-veg']) print(mlb.fit_transform(y_train)) You’re building a machine learning pipeline to preprocess data and train a model on a classification task. You decide to use a pipeline that includes data preprocessing and a support vector machine (SVM) classifier. The following code snippet demonstrates the pipeline creation and usage: from sklearn.pipeline import Pipeline from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler import numpy as np # Simulated data (features: X, target: y) X = np.array([[2, 3], [5, 7], [8, 10]]) y = np.array([0, 1, 0]) # Create a pipeline with StandardScaler and SVM classifier pipeline = Pipeline([ ('scaler', StandardScaler()), ('svm', SVC())]) # Fit the pipeline on training data pipeline.fit(X, y) # Make predictions using the trained pipeline predictions = pipeline.predict(X) What is the purpose of using the pipeline in this code snippet?