Table of Contents Introduction: What is overfitting? Causes of overfitting: How to detect overfitting? Avoiding the overfitting: Underfitting in machine learning: Causes of underfitting: How to avoid underfitting: Introduction: In machine learning, models are trained on training data to make accurate predictions on test data. The ideal model demonstrates strong performance on test data, indicating […]
Linear regression is a machine learning algorithm that models the relationship between the dependent and independent variables. It is used for supervised learning tasks where the labels are of continuous data type. Due to its simplicity and effectiveness this algorithm finds application in various fields, including economics, biology, engineering, and social sciences. Aim of algorithm […]
This article gives the details of methods to handle the missing values in data and their implementation using Python. Handling missing values is an important step in data preprocessing. It is important to handle the missing values as it reduces the accuracy and reliability of models and these values are not supported by most of […]
This article explains the boosting and bagging methods used in machine learning. How both the methods work and what are their advantages are discussed here. Boosting Boosting is an ensemble learning technique in which a number of weak classifiers are trained sequentially and each classifier tries to reduce the error of its predecessor classifier. How […]