When working with machine learning models, properly encoding categorical variables is crucial for model performance. Among categorical variables, ordinal variables present a unique challenge because they have an inherent order or hierarchy that must be preserved during encoding. This article explores the best practices for encoding ordinal variables in sklearn, providing practical guidance and examples … Read more
Polynomial regression is an extension of linear regression that allows for modeling non-linear relationships by introducing polynomial terms of the input features. Scikit-learn’s PolynomialFeatures class enables the transformation of input features into higher-degree polynomial terms, making it possible to fit non-linear patterns in data using linear models. This article provides a detailed guide on how … Read more
The Random Forest Classifier is one of the most powerful and widely used machine learning algorithms for classification tasks. Built on an ensemble of decision trees, it delivers excellent predictive accuracy while reducing the risk of overfitting. In Python, the scikit-learn (sklearn) library provides a robust and easy-to-use implementation of Random Forest. In this article, … Read more
Understanding which features are most influential in predicting your target variable is crucial for interpreting your machine learning model and improving its performance. In this guide, we’ll explore how to get feature importance using various methods in Scikit-learn (sklearn), a powerful Python library for machine learning. We’ll cover tree-based feature importance, permutation importance, and coefficients … Read more
Scikit-learn (sklearn) is one of the most popular machine learning libraries in Python. It provides simple and efficient tools for data mining and data analysis. In this blog post, we will delve into how to use sklearn for regression analysis, a key method for predicting continuous outcomes. What is Regression Analysis? Regression analysis is a … Read more
