Question

Write a single python file for multi-class classification:

Data Splitting: The dataset is available in the template file "A2_StudentMatriculationNumber.py", which contains 3 classes of training samples for classification. Please split the dataset into two sets: 70% of samples for training, and 30% of samples for testing. NOTE 1: Please use "from sklearn.model_selection import train_test_split" to split the data into the training and test sets.

Answer 1

To split the dataset into training and testing sets for multi-class classification in Python, you can use the train_test_split function from the sklearn.model_selection module.

Step-by-step explanation:

To split the dataset into training and testing sets for multi-class classification using Python, you can use the train_test_split function from the sklearn.model_selection module.

Here's an example:

1. Import the necessary module: from sklearn.model_selection import train_test_split
2. Load your dataset
3. Define your features and target variables
4. Split the data into training and testing sets: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
5. 70% of the samples will be assigned to the training set (X_train, y_train) and 30% of the samples will be assigned to the testing set (X_test, y_test)