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**Supervised learning** is a machine learning technique. Supervised learning algorithms use labeled data (i.e., data tagged with the correct outcome) to predict outcomes for unseen data.

# Steps

To develop a supervised learning model, follow the steps below:

1. Create a training dataset. For example, for handwritten character analysis, the dataset would include pictures of written characters and information on what character is on them.

2. Transform the input object (the pictures of handwritten characters in our case) into a feature vector. The feature vector contains some features that describe the object.

3. Determine the desired learning algorithm and run it on the training set.

4. Evaluate the accuracy of the model using the test dataset.

5. Deploy the model to predict the outcomes of unforeseen data.

The following illustration summarizes the steps involved in developing a supervised learning model:
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# Types

## Classification

**Classification** is used to predict a categorical variable. Common classification algorithms include Decision Trees.

## Regression

**Regression** is used to predict a numeric value. Common regression algorithms include Regression Trees.

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**Supervised learning** is a machine learning technique. Supervised learning algorithms use labeled data (i.e., data tagged with the correct outcome) to predict outcomes for unseen data.

# Steps

To develop a supervised learning model, follow the steps below:

1. Create a training dataset. For example, for handwritten character analysis, the dataset would include pictures of written characters and information on what character is on them.

2. Transform the input object (the pictures of handwritten characters in our case) into a feature vector. The feature vector contains some features that describe the object.

3. Determine the desired learning algorithm and run it on the training set.

4. Evaluate the accuracy of the model using the test dataset.

5. Deploy the model to predict the outcomes of unforeseen data.

The following illustration summarizes the steps involved in developing a supervised learning model:
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What is supervised learning?

 Supervised learning is a machine learning technique. Supervised learning algorithms use labeled data (i.e., data tagged with the correct outcome) to predict outcomes for unseen data. Steps To develop a supervised learning model, follow the steps below:   Create a training dataset. For example, for handwritten character analysis, the dataset would include pictures of written characters and information on what character is on them.   Transform the input object (the pictures of handwritten characters in our case) into a feature vector. The feature vector contains some features that describe the object.   Determine the desired learning algorithm and run it on the training set.   Evaluate the accuracy of the model using the test dataset.   Deploy the model to predict the outcomes of unforeseen data.   The following illustration summarizes the steps involved in developing a supervised learning model: 