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Understanding Parameters vs. Hyperparameters in Machine Learning
Published 14 May 2025
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In machine learning, understanding the distinction between parameters and hyperparameters is crucial for developing effective models. While both terms refer to aspects of a model, they serve different purposes and have different implications for the training process. This blog will clarify the concepts of parameters and hyperparameters, provide insights into specific hyperparameters used in various algorithms like Ridge, Lasso, Logistic Regression, Decision Trees, AdaBoost, KNN, and KMeans Clustering.
Parameters are the internal variables of a model that the learning algorithm optimizes during the training process. These values are learned directly from the data. For instance, in linear regression, the coefficients (weights) assigned to each feature are considered parameters. The model adjusts these parameters to minimize the prediction error based on training data.
In a linear regression model:
Y = beta_0 + beta_1 X_1 + beta_2 X_2 + ldots + beta_n X_n
Where:
Hyperparameters are the settings or configurations used to control the behavior of the learning algorithm. Unlike parameters, hyperparameters are not learned from the data; instead, they are defined before the training process begins. Choosing the right hyperparameters can significantly affect the performance of a model.
In the context of decision trees, hyperparameters such as max_depth
or min_samples_split
govern the complexity of the tree and influence how well the model generalizes to new data.
max_depth
):
min_samples_split
):
min_samples_leaf
):
n_estimators
):
learning_rate
):
n_clusters
):
max_iter
):
Understanding the difference between parameters and hyperparameters is fundamental for effectively building machine learning models. Parameters are adjusted by the model during training, whereas hyperparameters need to be set before the training process and play a critical role in controlling the learning process. Familiarity with the specific hyperparameters related to different algorithms, such as Ridge and Lasso regression, decision trees, and clustering methods, empowers practitioners to fine-tune their models for optimal performance.
Mastering these concepts will enable you to navigate the complexities of machine learning confidently and effectively.
Happy modeling!