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How computers recognize patterns and make decisions without being explicitly programmed

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machine learning

Answer explanation

Machine learning enables computers to recognize patterns and make decisions by learning from data, rather than being explicitly programmed. This encompasses both supervised and unsupervised learning, but the broader term is machine learning.

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The inputs that a model uses to make decisions

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features

Answer explanation

In machine learning, 'features' are the inputs used by a model to make decisions. They represent the characteristics or attributes of the data, while 'label' refers to the output or target variable.

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the output you are trying to decide or predict with a model

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label

Answer explanation

The 'label' refers to the output you aim to predict with a model, distinguishing it from 'features' which are the input variables. Thus, 'label' is the correct choice.

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When a human trains a model to learn with examples

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supervised learning

Answer explanation

Supervised learning occurs when a model is trained using labeled examples, allowing it to learn patterns and make predictions based on input data. This distinguishes it from unsupervised learning, which uses unlabeled data.

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a computer program designed to make a decision

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model

Answer explanation

A 'model' in computer programs refers to a system that makes decisions based on input data. It is trained using algorithms to recognize patterns and predict outcomes, making it the correct choice for the question.

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Finding patterns in data that doesn't have any labels

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unsupervised learning

Answer explanation

Unsupervised learning is the correct choice as it involves finding patterns in data without any labels, unlike supervised learning which requires labeled data.

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giving examples to a model so it can learn

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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 uses these examples to adjust its parameters and improve its performance on tasks.

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data that can be separated into groups

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categorical data

Answer explanation

Categorical data refers to data that can be divided into distinct groups or categories. This makes it the correct choice, as it directly describes data that can be separated into groups, unlike the other options.

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predicting a category based on other features

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classification

Answer explanation

The process of predicting a category based on other features is known as classification. It involves using input features to assign a label to a data point, making 'classification' the correct choice.

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data that can be counted or measured

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numerical data

Answer explanation

Numerical data refers to data that can be counted or measured, making it the correct choice. In contrast, categorical data represents categories or groups, while features and classification do not specifically pertain to measurable data.