Model Based Learning

Model Selection: Choosing an appropriate model that can capture the underlying relationships in your data. This could involve linear regression, decision trees, support vector machines, or deep learning models like convolutional neural networks (CNNs) or recurrent neural networks (RNNs).

Model Training: Using data to estimate the parameters of the chosen model. This typically involves optimization techniques to minimize prediction errors, such as gradient descent for neural networks or maximum likelihood estimation for statistical models.

Model Evaluation: Assessing how well the model performs on unseen data. Common metrics include accuracy, precision, recall, F1 score for classification tasks, and mean squared error, R-squared for regression tasks.

Model Deployment: Applying the trained model to make predictions on new data. This is often a critical step in real-world applications, where the model's predictions are used to inform decisions or automate tasks.

Model Interpretation: Understanding the insights provided by the model. This can involve analyzing feature importance, examining coefficients, or visualizing decision boundaries, depending on the type of model.

?