planetdestroyyer / hyperparameter-tuning-in-python

Hyperparameter tuning is the process of optimizing the hyperparameters of a machine learning model to improve its performance.

Hyperparameter tuning is the process of optimizing the hyperparameters of a machine learning model to improve its performance. Hyperparameters are configuration settings for a model that are not learned during training but are set before training begins. Examples include the learning rate in a neural network, the depth of a decision tree, or the number of clusters in a K-means algorithm.

There are several techniques for hyperparameter tuning, including:

1. Grid Search: This involves selecting a grid of hyperparameter values and evaluating the model performance for each combination.

2. Random Search: Instead of searching through a predefined grid, random search selects hyperparameter values randomly from specified distributions.

3. Bayesian Optimization: This method builds a probabilistic model of the objective function (model performance) and selects hyperparameters to maximize this model.

4. Gradient-based Optimization: Some advanced techniques use gradient descent to directly optimize hyperparameters based on the model's performance.

Hyperparameter tuning is crucial for achieving the best performance from a machine learning model and often involves a balance between computational resources and desired performance improvements.