### Are Parameters Known Prior to Training an ML Model?
The process of determining the parameters of a model is known as **training**. During training, the model learns from the data by iteratively adjusting its parameters to optimize a given objective function (also known as a loss function).
So – No, **parameters are not known prior to training** a machine learning model. Parameters are the internal values that the model learns during the training process by adjusting them to minimize error and improve the model’s predictions. The values of the parameters are determined by the training algorithm through optimization techniques.
For example:
– In **linear regression**, the parameters are the weights (coefficients) and intercept.
– In **neural networks**, the parameters are the weights and biases of the neurons in the network.
### How Are Parameters Figured Out for a Model?
The process of determining the parameters of a model is known as **training**. During training, the model learns from the data by iteratively adjusting its parameters to optimize a given objective function (also known as a loss function). Here’s how parameters are figured out:
#### 1. **Initialization of Parameters**
– Before training begins, parameters are usually initialized with random values or specific initialization strategies (e.g., Xavier initialization in neural networks).
– In simple models like linear regression, parameters can also be initialized to zero.
#### 2. **Define a Loss Function**
– A **loss function** quantifies the difference between the model’s predictions and the actual values in the training data. The goal of training is to minimize this loss.
– Examples:
– **Mean Squared Error (MSE)** for regression tasks.
– **Cross-Entropy Loss** for classification tasks.
#### 3. **Optimization Algorithms**
– Optimization algorithms adjust the parameters to minimize the loss function. They compute the gradient of the loss function with respect to the parameters and use that information to update the parameters.
Common optimization algorithms include:
– **Gradient Descent**: The most widely used optimization method. It adjusts the parameters in the direction of the negative gradient to minimize the loss.
– Variants include **Stochastic Gradient Descent (SGD)**, **Mini-batch Gradient Descent**, **Momentum-based Gradient Descent**, and **Adam** (adaptive moment estimation).
– **Newton’s Method**: Uses second-order derivatives (Hessian matrix) to find the optimal parameters. It’s more computationally expensive but can converge faster for certain problems.
#### 4. **Backpropagation (for Neural Networks)**
– In neural networks, the **backpropagation algorithm** is used to calculate the gradients of the loss function with respect to each parameter (weights and biases) through the network.
– During backpropagation, the gradient is computed by applying the chain rule to propagate the error backward through the network, from the output layer to the input layer.
#### 5. **Iterative Updates (Epochs)**
– The model iteratively updates its parameters over multiple passes through the training data (called **epochs**).
– After each iteration, the parameters are updated using the optimization algorithm to gradually minimize the loss function.
#### 6. **Convergence and Stopping Criteria**
– The training process continues until one of the following stopping criteria is met:
– The loss function converges (i.e., further updates do not significantly reduce the loss).
– The number of training epochs reaches a predefined limit.
– Early stopping is triggered to prevent overfitting, which halts training when the validation performance stops improving.
#### 7. **Regularization (Optional)**
– **Regularization techniques** can be applied during training to prevent overfitting by penalizing large parameter values.
– Examples include **L1** (Lasso) and **L2** (Ridge) regularization, which add a penalty term to the loss function that discourages overly complex models.
### Summary of the Process:
1. **Parameter Initialization**: Parameters are randomly or strategically initialized.
2. **Loss Function**: The model defines an objective (loss) function to minimize.
3. **Optimization Algorithm**: Algorithms like gradient descent adjust parameters iteratively.
4. **Training Iterations**: Parameters are updated over multiple epochs until convergence or stopping criteria are met.
5. **Regularization**: Optional techniques ensure parameters do not overfit the training data.
### Example:
In a **linear regression** model, the goal is to minimize the error between the predicted and actual values. The model’s parameters (weights and intercept) are updated iteratively by adjusting them to reduce the difference between the predicted and actual outputs, usually via gradient descent. After several iterations, the model converges to a set of parameters that yield the best predictions based on the data.
### Key Takeaway:
– **Parameters are learned during training**, and their values are determined by optimizing the model to minimize the error between its predictions and the actual target values. The optimization algorithm updates these parameters through repeated iterations to find the best configuration.