Training data and models in Intro to Computing - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is training data in machine learning?

Training data is the set of examples used to teach a machine learning model how to make predictions or decisions. It contains input data and the correct answers (labels).

beginner

What is a model in the context of machine learning?

A model is a mathematical or computational representation that learns patterns from training data to make predictions or decisions on new data.

beginner

Why do we split data into training and testing sets?

We split data to train the model on one part (training set) and check how well it works on unseen data (testing set). This helps us know if the model can generalize well.

intermediate

What does it mean when a model is overfitting?

Overfitting happens when a model learns the training data too well, including noise or mistakes, so it performs poorly on new data.

intermediate

How does training data quality affect the model?

Good quality training data helps the model learn correct patterns. Poor quality data with errors or bias can lead to wrong or unfair predictions.

What is the main purpose of training data?

A To clean the data

B To test the model's accuracy

C To teach the model how to make predictions

D To store the final results

Training data is used to teach the model the relationship between inputs and outputs.

What do we call the part of data used to check model performance on new data?

A Testing set

B Validation set

C Training set

D Input set

The testing set is used to evaluate how well the model performs on unseen data.

Which problem occurs when a model fits training data too closely and fails on new data?

A Underfitting

B Overfitting

C Data cleaning

D Normalization

Overfitting means the model learned noise or details that don't generalize well.

What is a model in machine learning?

A A visualization chart

B A set of rules written by humans

C A database of training examples

D A mathematical representation learned from data

A model is a learned mathematical or computational representation that makes predictions.

Why is training data quality important?

A It ensures the model learns correct patterns

B It reduces file size

C It speeds up the computer

D It changes the model type

High-quality data helps the model learn accurate and fair patterns.

Explain in your own words what training data and a model are, and how they work together.

Think about how you learn from examples and then use that knowledge.

Describe why splitting data into training and testing sets is important for building a good model.

Consider how you check your understanding by practicing and then testing yourself.