ML | Dummy variable trap in Regression Models Last Updated : 08 Sep, 2021 Summarize Comments Improve Suggest changes Share Like Article Like Report Before learning about the dummy variable trap, let's first understand what actually dummy variable is. Dummy Variable in Regression Models: In statistics, especially in regression models, we deal with various kinds of data. The data may be quantitative (numerical) or qualitative (categorical). The numerical data can be easily handled in regression models but we can't use categorical data directly, it needs to be transformed in some way. For transforming categorical attributes to numerical attributes, we can use the label encoding procedure (label encoding assigns a unique integer to each category of data). But this procedure is not alone that suitable, hence, One hot encoding is used in regression models following label encoding. This enables us to create new attributes according to the number of classes present in the categorical attribute i.e if there are n number of categories in categorical attribute, n new attributes will be created. These attributes created are called Dummy Variables. Hence, dummy variables are "proxy" variables for categorical data in regression models. These dummy variables will be created with one-hot encoding and each attribute will have a value of either 0 or 1, representing the presence or absence of that attribute. Dummy Variable Trap: The Dummy variable trap is a scenario where there are attributes that are highly correlated (Multicollinear) and one variable predicts the value of others. When we use one-hot encoding for handling the categorical data, then one dummy variable (attribute) can be predicted with the help of other dummy variables. Hence, one dummy variable is highly correlated with other dummy variables. Using all dummy variables for regression models leads to a dummy variable trap. So, the regression models should be designed to exclude one dummy variable. For Example - Let's consider the case of gender having two values male (0 or 1) and female (1 or 0). Including both the dummy variable can cause redundancy because if a person is not male in such case that person is a female, hence, we don't need to use both the variables in regression models. This will protect us from the dummy variable trap. Comment More infoAdvertise with us Next Article ML | Dummy variable trap in Regression Models D deepak_jain Follow Improve Article Tags : Machine Learning python Practice Tags : Machine Learningpython Similar Reads Python Tutorial - Learn Python Programming Language Python is one of the most popular programming languages. It’s simple to use, packed with features and supported by a wide range of libraries and frameworks. Its clean syntax makes it beginner-friendly. It'sA high-level language, used in web development, data science, automation, AI and more.Known fo 10 min read Machine Learning Tutorial Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. In simple words, ML teaches the systems to think and understand like humans by learning from the data.Machin 5 min read Linear Regression in Machine learning Linear regression is a type of supervised machine-learning algorithm that learns from the labelled datasets and maps the data points with most optimized linear functions which can be used for prediction on new datasets. It assumes that there is a linear relationship between the input and output, mea 15+ min read Support Vector Machine (SVM) Algorithm Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It tries to find the best boundary known as hyperplane that separates different classes in the data. It is useful when you want to do binary classification like spam vs. not spam or 9 min read Logistic Regression in Machine Learning Logistic Regression is a supervised machine learning algorithm used for classification problems. Unlike linear regression which predicts continuous values it predicts the probability that an input belongs to a specific class. It is used for binary classification where the output can be one of two po 11 min read 100+ Machine Learning Projects with Source Code [2025] This article provides over 100 Machine Learning projects and ideas to provide hands-on experience for both beginners and professionals. Whether you're a student enhancing your resume or a professional advancing your career these projects offer practical insights into the world of Machine Learning an 5 min read K means Clustering – Introduction K-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ 4 min read K-Nearest Neighbor(KNN) Algorithm K-Nearest Neighbors (KNN) is a supervised machine learning algorithm generally used for classification but can also be used for regression tasks. It works by finding the "k" closest data points (neighbors) to a given input and makesa predictions based on the majority class (for classification) or th 8 min read Backpropagation in Neural Network Back Propagation is also known as "Backward Propagation of Errors" is a method used to train neural network . Its goal is to reduce the difference between the model’s predicted output and the actual output by adjusting the weights and biases in the network.It works iteratively to adjust weights and 9 min read Introduction to Convolution Neural Network Convolutional Neural Network (CNN) is an advanced version of artificial neural networks (ANNs), primarily designed to extract features from grid-like matrix datasets. This is particularly useful for visual datasets such as images or videos, where data patterns play a crucial role. CNNs are widely us 8 min read Like