What are hyper parameters in machine learning?

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In machine learning, a hyperparameter is a parameter whose value is set before the learning process begins. Unlike model parameters (such as weights in neural networks or coefficients in linear regression) that are learned from the training data, hyperparameters are configuration settings used to control the training process and the structure of the model. Hyperparameters are typically specified by the practitioner and can significantly impact the performance of the model.

Types of Hyperparameters
1. Model-Specific Hyperparameters: These determine the structure of the model.

• Neural Networks: Number of layers, number of neurons per layer, activation functions, etc.
  Decision Trees: Maximum depth, minimum samples per leaf, criterion for splitting (e.g., Gini impurity or entropy).
  Support Vector Machines (SVMs): Kernel type (e.g., linear, polynomial, RBF), regularization parameter C.

2. Training Hyperparameters: These influence the training process.

• Learning Rate: The step size used in gradient descent to update the model parameters.
  Batch Size: The number of training samples used to compute each gradient update.
  Number of Epochs: The number of times the entire training dataset is passed through the model.

3. Regularization Hyperparameters: These help prevent overfitting.

• L1/L2 Regularization Coefficients: Parameters that control the strength of L1 (Lasso) and L2 (Ridge) regularization.
  Dropout Rate: The probability of dropping a neuron during training in neural networks.

4. Optimization Hyperparameters: These affect how the model is optimized.

• Optimizer Type: Choice of optimization algorithm (e.g., SGD, Adam, RMSprop).
  Momentum: Parameter for optimizers like SGD that helps accelerate gradients vectors in the right directions.

Hyperparameter Tuning

Finding the optimal set of hyperparameters is crucial for building an effective model. This process is known as hyperparameter tuning or optimization. Common techniques include:

Grid Search: An exhaustive search over a specified parameter grid. Each combination of hyperparameters is tried, and the model is evaluated for each combination.

Code: Select all

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier

param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20, 30],
    'min_samples_split': [2, 5, 10]
}

grid_search = GridSearchCV(RandomForestClassifier(), param_grid, cv=5)
grid_search.fit(X_train, y_train)
print(grid_search.best_params_)

Summary

Hyperparameters are critical settings in a machine learning model that need to be defined before the training process. Proper hyperparameter tuning can significantly improve model performance, and various techniques like grid search, random search, and Bayesian optimization can be used to find the optimal values.