Classification and Clustering are two types of learning methods which are used in Machine Learning. The main difference between them lies in their output.

1. Classification: It is a type of supervised learning where we teach or train the model using already tagged data. For example, let's say we have a basket of fruits and we want to classify them based on their type. We already know the types of fruits (like apple, banana, orange etc.) and we train our model using this data. So, when a new fruit is given to the model, it can classify it into one of the types it has been trained on.

2. Clustering: It is a type of unsupervised learning where we do not have any tagged data. The model learns on its own by discovering information. It groups the data into different clusters based on similarity and other techniques. For example, let's say we have a group of people and we want to group them based on their income. We do not have any pre-defined groups. The model will group these people into different clusters based on their income.

In summary, Classification is about predicting a label and involves training the model on pre-labeled (classified) data, while Clustering is about grouping similar entities together without any prior knowledge.

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Classification and Clustering are two types of learning methods which are used in Machine Learning. The main difference between them lies in their output.

1. Classification: It is a type of supervised learning where we teach or train the model using already tagged data. For example, let's say we have a basket of fruits and we want to classify them based on their type. We already know the types of fruits (like apple, banana, orange etc.) and we train our model using this data. So, when a new fruit is given to the model, it can classify it into one of the types it has been trained on.

2. Clustering: It is a type of unsupervised learning where we do not have any tagged data. The model learns on its own by discovering information. It groups the data into different clusters based on similarity and other techniques. For example, let's say we have a group of people and we want to group them based on their income. We do not have any pre-defined groups. The model will group these people into different clusters based on their income.

In summary, Classification is about predicting a label and involves training the model on pre-labeled (classified) data, while Clustering is about grouping similar entities together without any prior knowledge.

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Classification and Clustering are two types of learning methods which are used in Machine Learning. The main difference between them lies in their output.

1. Classification: It is a type of supervised learning where we teach or train the model using already tagged data. For example, let's say we have a basket of fruits and we want to classify them based on their type. We already know the types of fruits (like apple, banana, orange etc.) and we train our model using this data. So, when a new fruit is given to the model, it can classify it into one of the types it has been trained on.

2. Clustering: It is a type of unsupervised learning where we do not have any tagged data. The model learns on its own by discovering information. It groups the data into different clusters based on similarity and other techniques. For example, let's say we have a group of people and we want to group them based on their income. We do not have any pre-defined groups. The model will group these people into different clusters based on their income.

In summary, Classification is about predicting a label and involves training the model on pre-labeled (classified) data, while Clustering is about grouping similar entities together without any prior knowledge.

What is the main difference between classification and clustering? Explain using concrete examples.

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