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 means it learns the relationship between the input and output.

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

4. The model's predictions are then compared to the actual output. If the predictions are incorrect, the model adjusts its parameters and the training process is repeated.

5. This process continues until the model's predictions are as accurate as possible.

6. The trained model can then be used to make predictions or decisions based on new input data.

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 means it learns the relationship between the input and output.

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

4. The model's predictions are then compared to the actual output. If the predictions are incorrect, the model adjusts its parameters and the training process is repeated.

5. This process continues until the model's predictions are as accurate as possible.

6. The trained model can then be used to make predictions or decisions based on new input data.

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 means it learns the relationship between the input and output.

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

4. The model's predictions are then compared to the actual output. If the predictions are incorrect, the model adjusts its parameters and the training process is repeated.

5. This process continues until the model's predictions are as accurate as possible.

6. The trained model can then be used to make predictions or decisions based on new input data.

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

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