Hyperparameter Optimization in Regression Analysis - Part 1
Introduction:
In the previous tutorial, we discussed Lasso, Ridge, and Elastic Net regressions. In this tutorial, we will focus on the basics of how to optimize these models through hyperparameter tuning.
Part I: Understanding Hyperparameters
1.1: What are Hyperparameters?
Hyperparameters are parameters that are not learned from data. They are set prior to the commencement of the learning process and help to control the behavior of the model. For instance, the ‘alpha’ in our previous tutorial is a hyperparameter.
Part II: Methods for Hyperparameter Tuning
2.1: Grid Search
Grid search is a tuning technique that attempts to compute the optimum values of hyperparameters. It is an exhaustive search that is performed on the specific parameter values of a model.
from sklearn.model_selection import GridSearchCV
# Define the grid of hyperparameters 'params'
params = {'alpha': [0.02, 0.024, 0.025, 0.026, 0.03]}
# Instantiate a 10-fold CV grid search object 'grid'
grid = GridSearchCV(estimator=lasso, param_grid=params, cv=10)
# Fit the grid search
grid.fit(X_train, y_train)2.2: Random Search
Random search differs from grid search in that it does not try every parameter value. Instead, it randomly selects values within the ranges specified.
from sklearn.model_selection import RandomizedSearchCV
# Define the grid of hyperparameters 'params'
params = {'alpha': [0.02, 0.024, 0.025, 0.026, 0.03]}
# Instantiate a 10-fold CV randomized search object 'random'
random = RandomizedSearchCV(estimator=ridge, param_distributions=params, cv=10)
# Fit the random search
random.fit(X_train, y_train)Part III: Hyperparameter Tuning for Regression Models
3.1: Tuning Lasso Regression
When tuning Lasso Regression, the main hyperparameter to adjust is ‘alpha’, which controls the amount of shrinkage: the larger the value of alpha, the greater the amount of shrinkage.
3.2: Tuning Ridge Regression
In Ridge Regression, ‘alpha’ is also the primary hyperparameter. However, the impact is slightly different: alpha reduces the complexity of the model by coefficient shrinkage.
3.3: Tuning Elastic Net Regression
Elastic Net has two key hyperparameters: ‘alpha’ and ‘l1_ratio’. ‘Alpha’ is the penalty term and ‘l1_ratio’ is the mix between L1 and L2 regularization.
Conclusion:
Hyperparameter tuning is crucial in building optimal machine learning models. Although it can be a time-consuming process, Python, with libraries such as Scikit-Learn, provides efficient tools for carrying out this process effectively. Tuning your models can lead to significant improvements in model performance.