Parameters vs. Hyperparameters
In machine learning, you will often hear the terms parameters and hyperparameters. Understanding the difference between these two is crucial for building, configuring, and improving models. Parameters are values that a model learns automatically from the training data; they define the internal state of the model after it has seen the data. Hyperparameters, on the other hand, are external settings that you choose before training begins; they control aspects of the learning process or the structure of the model itself. The distinction is important because while parameters are optimized automatically by the learning algorithm, hyperparameters must be set by you and can significantly influence the model's performance.
Parameters are learned from data during training (for example, the weights in linear regression). Hyperparameters are set before training and control the learning process (such as the learning rate or the number of trees).
12345678910111213141516171819from sklearn.linear_model import LogisticRegression from sklearn.datasets import load_iris # Load a simple dataset X, y = load_iris(return_X_y=True) # Create a LogisticRegression model with specific hyperparameters model = LogisticRegression(C=0.1, solver="liblinear") # C and solver are hyperparameters # Train the model (fit learns the parameters from data) model.fit(X, y) # Accessing learned parameters print("Model coefficients (parameters):\n", model.coef_) print("Model intercepts (parameters):", model.intercept_) # Accessing hyperparameters print("Hyperparameter C:", model.C) print("Hyperparameter solver:", model.solver)
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In machine learning, you will often hear the terms parameters and hyperparameters. Understanding the difference between these two is crucial for building, configuring, and improving models. Parameters are values that a model learns automatically from the training data; they define the internal state of the model after it has seen the data. Hyperparameters, on the other hand, are external settings that you choose before training begins; they control aspects of the learning process or the structure of the model itself. The distinction is important because while parameters are optimized automatically by the learning algorithm, hyperparameters must be set by you and can significantly influence the model's performance.
Parameters are learned from data during training (for example, the weights in linear regression). Hyperparameters are set before training and control the learning process (such as the learning rate or the number of trees).
12345678910111213141516171819from sklearn.linear_model import LogisticRegression from sklearn.datasets import load_iris # Load a simple dataset X, y = load_iris(return_X_y=True) # Create a LogisticRegression model with specific hyperparameters model = LogisticRegression(C=0.1, solver="liblinear") # C and solver are hyperparameters # Train the model (fit learns the parameters from data) model.fit(X, y) # Accessing learned parameters print("Model coefficients (parameters):\n", model.coef_) print("Model intercepts (parameters):", model.intercept_) # Accessing hyperparameters print("Hyperparameter C:", model.C) print("Hyperparameter solver:", model.solver)
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