“Demystifying Hyperparameter Tuning: GridSearchCV and RandomizedSearchCV”
“Finding the Optimal Model Configuration for Improved Machine Learning Performance”
Agenda
Basic Hyperparameter Tuning
- GridSearchCV
- RandomizedSearchCV
Later on, we will cover advanced hyperparameter tuning when we learn the XGBoost algorithm. Please wait for that.
Let’s start with this; it’s going to be a very important interview question.
Parameter Vs Hyperparameter Tuning
Parameters: Parameters are the internal variables of a model that are learned from the data during the training process. They define the model’s representation of the underlying patterns in the data. For example:
- In a linear regression model, the parameters are the coefficients of the predictors.
- In a neural network, the parameters are the weights and biases of the nodes.
- In a decision tree, the parameters are the split points and split criteria at each node.
The goal of the training process is to find the optimal values for these parameters, which minimize the discrepancy between the model’s predictions and the actual outcomes.
Hyperparameters: In machine learning, hyperparameters are parameters whose values are set before the learning process begins. These parameters are not learned from the data and must be predefined. They help in controlling the learning process and can significantly influence the performance of the model. For example:
- In a neural network, hyperparameters might include the learning rate, the number of layers in the network, or the number of nodes in each layer.
- In a support vector machine, the regularization parameter C or the kernel type can be considered as hyperparameters.
- In a decision tree, the maximum depth of the tree is a hyperparameter.
The best values for hyperparameters often cannot be determined in advance and must be found through trial and error.
Why the word ‘hyper’?
The choice of the word is primarily a naming convention to differentiate between the two types of values (internal parameters and guiding parameters) that influence the behavior of a machine learning model. It’s also a nod to the fact that the role they play is a meta one, in the sense that they control the structural aspects of the learning process itself rather than being part of the direct pattern-finding mission of the model.
GridSearchCV
GridSearchCV is a technique used in machine learning to systematically search for the best combination of hyperparameters for a specific algorithm. It helps in finding the optimal set of hyperparameters that yield the best performance for a given model.
To understand GridSearchCV, let’s consider an example where we want to use the K-Nearest Neighbors (KNN) algorithm to predict job placements based on IQ scores and CGPA (Cumulative Grade Point Average). We have three hyperparameters: metric (l1 or l2 distance), and the number of neighbors (3, 5, or 10).
GridSearchCV works by exhaustively trying all possible combinations of hyperparameters and evaluating the model’s performance using cross-validation. Cross-validation helps in assessing the model’s performance on multiple subsets of the training data, reducing the risk of overfitting.
In our example, we can create a grid table with the following combinations of hyperparameters:
GridSearchCV will train and evaluate the KNN algorithm using each combination of hyperparameters. It will measure the model’s performance, such as accuracy or any other chosen metric, using cross-validation. The algorithm will find the combination of hyperparameters that gives the best performance on average across all cross-validation folds.
Performing GridSearchCV with cross-validation can be time-consuming, especially if you have a large number of hyperparameters and a large dataset. However, it is worth the effort as it helps in identifying the best model configuration for optimal performance. If you have enough time and computational resources, it is recommended to use GridSearchCV for hyperparameter tuning to get the best possible results.
RandomizedSearchCV
RandomizedSearchCV is another technique used for hyperparameter tuning in machine learning. It is similar to GridSearchCV but works in a slightly different way.
In RandomizedSearchCV, instead of exhaustively searching through all possible combinations of hyperparameter values like GridSearchCV, it randomly samples a specified number of combinations from a defined search space of hyperparameters.
Let’s continue with the previous example of using the K-Nearest Neighbors (KNN) algorithm to predict placement outcomes based on IQ, CGPA, and Placement scores. In RandomizedSearchCV, you would still specify the hyperparameters to be tuned, such as the metric and the number of neighbors. However, instead of creating a grid or table of all possible values, you define a range or distribution for each hyperparameter.
For example, you can specify a range of values for the number of neighbors, such as between 1 and 20, and a distribution for the metric, such as a choice between “l1” and “l2”. RandomizedSearchCV will then randomly select a set number of combinations from these defined ranges or distributions.
During the training process, RandomizedSearchCV will evaluate the performance of each randomly selected combination using cross-validation. It will iterate through the specified number of random combinations, train and test the KNN algorithm with each combination, and measure the performance using the chosen evaluation metric.
The advantage of RandomizedSearchCV over GridSearchCV is that it allows you to explore a wider range of hyperparameter values in a more efficient way. Since it randomly samples combinations, it can be useful when you have a large search space or when the search space is not well-defined.
However, it is important to note that RandomizedSearchCV does not guarantee that it will find the optimal combination of hyperparameters. It is more exploratory in nature and may not cover all possible combinations. Nevertheless, it can be a good option when you have limited time and resources for hyperparameter tuning.
In summary, RandomizedSearchCV is a technique that randomly selects combinations of hyperparameters from defined search spaces to find the best set of hyperparameters for your machine learning algorithm. It offers an alternative approach to hyperparameter tuning compared to GridSearchCV, allowing for more efficient exploration of hyperparameter values.
Can this be improved?
advanced hyperparameter tuning when we learn the XGBoost algorithm. Please wait for that…...