The type of learning in which labeled training data is used is called Supervised Learning. Here are the steps to identify it:

1. Understand the definition: Supervised learning is a type of machine learning where the model is trained on a labeled dataset. A labeled dataset is a group of data that has been tagged with one or more labels. In other words, the labels are the answers the machine learning model is being trained to predict.

2. Look for key characteristics: In a supervised learning model, an algorithm learns from labeled training data, and uses this learning to predict the labels of new or unseen data. The training process continues until the model achieves a desired level of accuracy on the training data.

3. Examples of Supervised Learning: Some common examples of supervised learning include classification problems like spam detection (where the labels could be 'spam' or 'not spam') and regression problems like predicting house prices (where the labels could be numerical values representing the price of the house).

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The type of learning in which labeled training data is used is called Supervised Learning. Here are the steps to identify it:

1. Understand the definition: Supervised learning is a type of machine learning where the model is trained on a labeled dataset. A labeled dataset is a group of data that has been tagged with one or more labels. In other words, the labels are the answers the machine learning model is being trained to predict.

2. Look for key characteristics: In a supervised learning model, an algorithm learns from labeled training data, and uses this learning to predict the labels of new or unseen data. The training process continues until the model achieves a desired level of accuracy on the training data.

3. Examples of Supervised Learning: Some common examples of supervised learning include classification problems like spam detection (where the labels could be 'spam' or 'not spam') and regression problems like predicting house prices (where the labels could be numerical values representing the price of the house).

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The type of learning in which labeled training data is used is called Supervised Learning. Here are the steps to identify it:

1. Understand the definition: Supervised learning is a type of machine learning where the model is trained on a labeled dataset. A labeled dataset is a group of data that has been tagged with one or more labels. In other words, the labels are the answers the machine learning model is being trained to predict.

2. Look for key characteristics: In a supervised learning model, an algorithm learns from labeled training data, and uses this learning to predict the labels of new or unseen data. The training process continues until the model achieves a desired level of accuracy on the training data.

3. Examples of Supervised Learning: Some common examples of supervised learning include classification problems like spam detection (where the labels could be 'spam' or 'not spam') and regression problems like predicting house prices (where the labels could be numerical values representing the price of the house).

Identify the type of learning in which labeled training data is used.

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