We+need+to+divide+the+total+dataset+into+three+sets+of+data+for+classification:+‘Training+ data’,+‘

To divide the total dataset into three sets of data for classification, we typically use the following sets: 'Training data', 'Validation data', and 'Test data'.

1. Training data: This set is used to train the classification model. It is the largest set and is used to learn the patterns and relationships in the data.

2. Validation data: This set is used to fine-tune the model and select the best hyperparameters. It is used to evaluate the model's performance on unseen data and make adjustments to improve its accuracy.

3. Test data: This set is used to evaluate the final performance of the trained model. It is used to assess how well the model generalizes to new, unseen data. The test data should be representative of the real-world data that the model will encounter.

Here is an example of how to divide the dataset into these three sets using Python:

from sklearn.model_selection import train_test_split

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Split the training data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)

In this example, the `train_test_split` function from the `sklearn.model_selection` module is used to split the data. The `test_size` parameter determines the proportion of the data that will be allocated to the test set.