The AI technique that involves training a model on a large dataset to make predictions or decisions is Supervised Learning.

Here's a step-by-step explanation:

1. In Supervised Learning, we start with a known set of input data and known responses to the data (output).

2. The model is trained on this data, which involves adjusting the parameters of the model to improve its predictions. This is done using various algorithms like linear regression, decision trees, etc.

3. Once the model is trained, it can be used to predict the output for new, unknown data.

4. The model's performance is evaluated using a test set of data that the model has not been trained on. The better the model's predictions on the test set, the better the model is considered to be.

5. The model can be retrained with new data as it becomes available, allowing it to improve and adapt over time.

So, the correct option is Supervised Learning.

Question

The AI technique that involves training a model on a large dataset to make predictions or decisions is Supervised Learning.

Here's a step-by-step explanation:

1. In Supervised Learning, we start with a known set of input data and known responses to the data (output).

2. The model is trained on this data, which involves adjusting the parameters of the model to improve its predictions. This is done using various algorithms like linear regression, decision trees, etc.

3. Once the model is trained, it can be used to predict the output for new, unknown data.

4. The model's performance is evaluated using a test set of data that the model has not been trained on. The better the model's predictions on the test set, the better the model is considered to be.

5. The model can be retrained with new data as it becomes available, allowing it to improve and adapt over time.

So, the correct option is Supervised Learning.

Knowee AI · Accepted Answer

The AI technique that involves training a model on a large dataset to make predictions or decisions is Supervised Learning.

Here's a step-by-step explanation:

1. In Supervised Learning, we start with a known set of input data and known responses to the data (output).

2. The model is trained on this data, which involves adjusting the parameters of the model to improve its predictions. This is done using various algorithms like linear regression, decision trees, etc.

3. Once the model is trained, it can be used to predict the output for new, unknown data.

4. The model's performance is evaluated using a test set of data that the model has not been trained on. The better the model's predictions on the test set, the better the model is considered to be.

5. The model can be retrained with new data as it becomes available, allowing it to improve and adapt over time.

So, the correct option is Supervised Learning.

Which AI technique involves training a model on a large dataset to make predictions or decisions?OptionsReinforcement learningUnsupervised learningGenetic algorithmsSupervised learning

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