Encoding

3 Key Encoding Techniques for Machine Learning: A Beginner-Friendly Guide with Pros, Cons, and Python Code ExamplesHow should we choose between label, one-hot, and target encoding?Why Do We Need Encoding?In the realm of machine learning, most algorithms demand inputs in numeric form, especially in many popular Python frameworks. For instance, in scikit-learn, linear regression, and neural networks require numerical variables. This means we need to transform categorical variables into numeric ones for these models to…

Data comes in different shapes and forms. One of those shapes and forms is known as categorical data.This poses a problem because most Machine Learning algorithms use only numerical data as input. However, categorical data is usually not a challenge to deal with, thanks to simple, well-defined functions that transform them into numerical values. If you have taken any data science course, you will be familiar with the one hot encoding strategy for categorical features. This strategy is great when your features have limited…

Why we can’t arbitrarily encode categorical featuresContinue reading on Towards Data Science » Why we can’t arbitrarily encode categorical featuresContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their authors. If you are the…

Exploring the Impact of Encoding Protected Attributes on Fairness in MLLady Justice at dawn from UnsplashWe will explore the world of categorical attribute encoding and its implications for machine learning models in terms of accuracy and fairness. Categorical attributes, such as country of birth or ethnicity, play a crucial role in determining the presence of sensitive information in data. However, many machine learning algorithms struggle to directly process categorical attributes, necessitating the use of encoding…

QuEra Computing, creator of the world’s first neutral-atom quantum computer named Aquila, in collaboration with researchers from Harvard and Innsbruck Universities, has revealed a novel method for performing a broader range of optimization calculations on neutral-atom machines. The findings overcome the native connectivity limitations of the qubits in Rydberg atom arrays, enabling them to solve more complex optimization problems, including maximum independent sets on graphs with arbitrary connectivity and quadratic…

Scikit Learn or Pandas?One hot encoding is a popular method to represent categorical data (All images by author)Both sklearn.preprocessing.OneHotEncoder and pandas.get_dummies are popular choices (well, practically the only choices unless you want want to implement it yourself) to perform One Hot Encoding. Most scientist recommend scikit, as using its fit/transform paradigm it provides a built-in mechanism to learn all the possible categories from the training set and apply them to the validation or real input data.…

A real-world test on MassMutual’s production modelCo-authored with Dmytro KarabashPhoto by Jason Pogacnik on UnsplashIn our previous article, “Hidden Data Science Gem: Rainbow Method for Label Encoding”, we discussed the advantages of using label encoding over one-hot encoding for categorical variables, especially when developing tree-based models. We introduced the Rainbow method, which helps identify the most appropriate ordinal encoding for different types of categorical variables.In this article, we will continue…

A closer look at the technicalities and practical applicationsIn this article we will cover the topic of character encoding standards, specifically focusing on the ASCII and Unicode systems. We will dive into how they work and their role in deep learning. In addition, we will provide some examples of character encoding using Tensorflow, to have an overview of how this library manages strings on the inside.Photo by Giammarco on UnsplashBut first of all, we will present some important concepts.Character encoding is a system…

6 feature encoding techniques to consider for your data science workflowsPhoto by Susan Holt Simpson on UnsplashFeature Encoding converts categorical variables to numerical variables as part of the feature engineering step to make the data compatible with Machine Learning models. There are various ways to perform feature encoding, depending on the type of categorical variable and other considerations.This article introduces tips to perform feature encoding in general, elaborating on 6 feature encoding techniques that you…

Data cleaning is necessary. I believe most of us agree on that. A project will usually begin with some exploration and cleaning before we can go to the modeling part.Actually, I would say that most of a Data Scientist’s work is done between cleaning and transforming the dataset.The problem to be solved in this quick tutorial is for us to deal with variable encoding. Most of the machine learning algorithms expect numbers instead of text to estimate something. After all, computers are logical machines that rely on numbers…