Explain what is the function of ‘Supervised Learning’?

The function of supervised learning is to train a model to learn the mapping between input data and corresponding output labels based on a labeled dataset. In supervised learning, the algorithm is provided with a dataset consisting of input-output pairs, where the inputs are features or attributes, and the outputs are the labels or target values. The primary goal is for the model to learn the relationship between the input features and the output labels so that it can make accurate predictions or decisions when presented with new, unseen data.

The key steps involved in supervised learning are:

1. Data Collection: Gathering a dataset containing input-output pairs.
2. Data Preprocessing: Cleaning the data, handling missing values, and transforming features if necessary.
3. Model Selection: Choosing an appropriate algorithm or model architecture based on the problem at hand and the characteristics of the data.
4. Training: Using the labeled dataset to train the model, where the model learns the mapping between input features and output labels by adjusting its parameters.
5. Evaluation: Assessing the performance of the trained model on a separate validation or test dataset to measure its accuracy and generalization capability.
6. Prediction: Deploying the trained model to make predictions or decisions on new, unseen data.

Supervised learning is widely used in various applications such as classification (e.g., spam detection, image recognition), regression (e.g., house price prediction, stock market forecasting), and other predictive modeling tasks where there’s a clear distinction between input features and output labels.