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Selecting the Best Model for Boston Housing Dataset using Cross-Validation in Python
Machine learning is a rapidly evolving field that provides powerful tools for data analysis and prediction. One of the key tasks in machine learning is model selection, which involves choosing the best model from a set of candidate models. In this article, we will explore how to use cross-validation to select the best model for the Boston Housing dataset, a commonly used dataset in machine learning.
The Boston Housing dataset contains information about housing prices in Boston and various factors that may affect the prices, such as crime rate, air pollution, and distance to employment centers. The dataset consists of 506 samples, each with 13 features and a target variable, the median value of owner-occupied homes in thousands of dollars.
To load the Boston Housing dataset in Python, we can use the scikit-learn library, a popular machine learning library that provides various tools for data analysis and model selection. The code snippet below shows how to load the dataset and split it into features and target variables:
from sklearn.datasets import load_boston
# Load the Boston Housing dataset
boston = load_boston()
X, y = boston.data, boston.targetOnce we have loaded the dataset, we can define a list of candidate models to evaluate. In this example, we will consider three models: Linear Regression, Ridge Regression, and Lasso Regression. Linear Regression is a simple model that fits a…
