- overfitting & underfitting
- Fitting in machine learning refers to the process of training a model on a dataset so that it can make predictions or perform a specific task. Fitting is a key step in the machine learning pipeline, as it is the process of creating the model that will be used for prediction or inference.
- The goal of fitting a model is to find the model parameters that best describe the relationship between the input data and the output variable. This is typically done by minimizing a loss function, which measures the difference between the predicted output and the true output for each data point in the training dataset. The loss function is a measure of the model’s performance, and the goal is to find the model parameters that minimize the loss function.
- In supervised learning, fitting involves training a model on labeled data, where the input data is paired with corresponding output labels. The model is trained to learn the relationship between the input data and the output labels, so that it can make accurate predictions on new, unseen data.
- In unsupervised learning, fitting involves learning the underlying structure of the data without explicit labels. The goal is to find patterns, relationships, and structure in the data that can be used for clustering, anomaly detection, or other tasks.
- Fitting a model typically involves choosing a suitable model architecture or algorithm, selecting appropriate hyperparameters, and optimizing the model parameters using an optimization algorithm, such as gradient descent. The process of fitting a model often involves a trade-off between model complexity and generalization performance. A more complex model may fit the training data well, but may not generalize well to new, unseen data.
- Overfitting and underfitting are common problems that can occur during the fitting process. Overfitting occurs when the model is too complex and fits the training data too closely, leading to poor generalization performance on new data. Underfitting occurs when the model is too simple and does not capture the underlying structure of the data, leading to poor performance on both the training and test data. Regularization techniques and cross-validation can be used to prevent overfitting and underfitting, respectively.