Python for Deep Learning - Build Neural Networks in Python - Training and Test Sets: Splitting Data

The video tutorial explains the process of constructing machine learning algorithms that can learn from data and make predictions. It emphasizes the importance of dividing data into training and testing sets, with a recommended split of 80-90% for training. The tutorial demonstrates how to implement this using the train_test_split method from the scikit-learn library in a Jupyter Notebook, ensuring consistent data splitting with a fixed random state.

What is the primary purpose of training data in machine learning?

• To validate the model's predictions

• To split the data into smaller subsets

• To build and train the machine learning algorithm

• To test the model's performance on unseen data

Why is it important to split data into training and test sets?

• To reduce the number of variables

• To make the data more complex

• To increase the size of the dataset

• To ensure the model is not overfitting

What percentage of data is typically recommended for training in a machine learning model?

• 80-90%

• 10-20%

• 50-60%

• 30-40%

Which library provides the train_test_split method for data splitting?

• NumPy

• Pandas

• scikit-learn

• TensorFlow

What does setting the random_state parameter in train_test_split ensure?

• The data is split randomly every time

• The data is split in the same way each time

• The data is split into equal parts

• The data is not split at all