## Loading Runtime

A cost function, also known as a loss function or objective function, is a crucial component in machine learning models. It quantifies the difference between the predicted values of a model and the actual values (ground truth) in the training data. The goal during the training process is to minimize this cost function, as doing so leads to a model that makes more accurate predictions.

The cost function serves as a measure of how well the model is performing and guides the optimization process. The optimization algorithm adjusts the model's parameters or weights iteratively to reduce the cost function, bringing the predicted outputs closer to the actual values. The process of minimizing the cost function is often referred to as training or fitting the model.

The choice of the cost function depends on the type of machine learning task. Here are a few examples for common tasks:

**Regression: Mean Squared Error (MSE)**

For regression problems where the goal is to predict continuous values, the mean squared error is often used as the cost function. It calculates the average squared difference between the predicted values and the true values.

**Classification: Cross-Entropy Loss (Log Loss)**

For binary or multiclass classification problems, cross-entropy loss, also known as log loss, is commonly used. It measures the difference between the predicted probability distribution and the true distribution of class labels.

**Support Vector Machines: Hinge Loss**

In the context of support vector machines (SVMs), the hinge loss is often used. It penalizes the model more severely for misclassifying examples that are closer to the decision boundary. The optimization process involves adjusting the model parameters (weights) to minimize the cost function. Techniques like gradient descent are commonly used for this purpose. The gradient of the cost function indicates the direction in which the parameters should be adjusted to reduce the cost.

The effectiveness of a machine learning model is often assessed based on its performance on a validation set or test set after training. The choice of an appropriate cost function is critical, as it directly influences the learning process and the model's ability to generalize to new, unseen data.