Lesson 3: Types of Machine Learning Code.org

Front

What are the inputs that a model uses to make decisions called?

Back

Features

Answer explanation

The inputs that a model uses to make decisions are called features. Features represent the characteristics or attributes of the data that help the model learn and make predictions.

Front

What is the output you are trying to decide or predict with a model called?

Back

Label

Answer explanation

The output you are trying to decide or predict with a model is called the 'Label'. Features are the input variables, while the model is the algorithm used. Training refers to the process of teaching the model.

Front

What is a computer program designed to make a decision called?

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Model

Answer explanation

A computer program designed to make a decision is called a 'Model'. In machine learning, a model is trained on data to make predictions or decisions based on input, distinguishing it from features, labels, or training processes.

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What is it called when a human trains a model to learn with examples?

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Supervised Learning

Answer explanation

When a human trains a model using labeled examples, it is called Supervised Learning. This method involves providing the model with input-output pairs to learn from, enabling it to make predictions on new data.

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What is the process of giving examples to a model so it can learn called?

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Training

Answer explanation

The process of giving examples to a model so it can learn is called Training. During training, the model adjusts its parameters based on the provided examples to improve its performance on tasks.

Front

What is finding patterns in data that doesn't have any labels called?

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Unsupervised Learning

Answer explanation

Finding patterns in unlabeled data is known as Unsupervised Learning. Unlike Supervised Learning, which uses labeled data, Unsupervised Learning identifies hidden structures without predefined categories.