Split Data for Machine Learning

The video tutorial covers data splitting techniques in machine learning, including train-test split and cross-validation using K-Fold. It demonstrates how to import data using pandas, manually split data, and create synthetic datasets. The tutorial also explains the importance of maintaining separate datasets for training, validation, and testing to ensure model accuracy and avoid overfitting. Additionally, it outlines the data engineering workflow, emphasizing data collection, feature engineering, and hyperparameter optimization.

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

• To reduce the size of the dataset

• To ensure data privacy

• To evaluate model performance on unseen data

• To increase computational efficiency

Which method is used to split data when the order of data matters, such as in time series?

• Train-test split with shuffle=False

• Train-test split with shuffle=True

• Cross-validation

• Random sampling

When manually splitting data, what is crucial to remember for sequential data?

• Split data into equal parts

• Always shuffle the data

• Use a fixed random seed

• Maintain the order of data

What is the advantage of using numpy arrays over pandas data frames for data splitting?

• Numpy arrays automatically handle missing values

• Numpy arrays allow for more complex data types

• Numpy arrays are more memory efficient

• Numpy arrays are easier to visualize

What is the purpose of using K-fold cross-validation?

• To test the model on multiple subsets of data

• To ensure data is shuffled

• To increase the size of the dataset

• To reduce the number of features

In K-fold cross-validation, what does the 'K' represent?

• The number of features

• The number of classifiers

• The number of data points

• The number of splits

Why is it important to maintain separate datasets for training and testing?

• To simplify data preprocessing

• To reduce data redundancy

• To prevent data leakage

• To ensure faster computation

What is hyperparameter optimization used for in machine learning?

• To clean the dataset

• To visualize data distributions

• To find the best model parameters

• To increase dataset size

What indicates that a model might be overfitting during training?

• The training loss decreases while test loss increases

• The test loss decreases

• The training loss increases

• Both training and test losses decrease

What is the role of a digital twin in data engineering?

• To split data

• To visualize data

• To generate synthetic data

• To clean data