XGBoost has gained widespread recognition for its impressive performance in numerous Kaggle competitions, making it a favored choice for tackling complex machine learning challenges. Known for its efficiency in handling large datasets, this powerful algorithm stands out for its practicality and effectiveness. In this post, we will apply XGBoost to the Ames Housing dataset to …
The AI industry is rapidly advancing towards creating solutions using large language models (LLMs) and maximizing the potential of AI models. Companies are seeking tools that seamlessly integrate AI into existing codebases without the hefty costs associated with hiring professionals and acquiring resources. This is where Controlflow comes into play. With ControlFlow, you can develop …
Ensemble learning techniques primarily fall into two categories: bagging and boosting. Bagging improves stability and accuracy by aggregating independent predictions, whereas boosting sequentially corrects the errors of prior models, improving their performance with each iteration. This post begins our deep dive into boosting, starting with the Gradient Boosting Regressor. Through its application on the Ames …
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AI applications are everywhere. I use ChatGPT on a daily basis — to help me with work tasks, and planning, and even as an accountability partner. Generative AI hasn’t just transformed the way we work. It helps businesses streamline operations, cut costs, and improve efficiency. As companies rush to implement generative AI solutions, there has been an …
This post dives into the application of tree-based models, particularly focusing on decision trees, bagging, and random forests within the Ames Housing dataset. It begins by emphasizing the critical role of preprocessing, a fundamental step that ensures our data is optimally configured for the requirements of these models. The path from a single decision tree …
Read more “From Single Trees to Forests: Enhancing Real Estate Predictions with Ensembles”
Categorical variables are pivotal as they often carry essential information that influences the outcome of predictive models. However, their non-numeric nature presents unique challenges in model processing, necessitating specific strategies for encoding. This post will begin by discussing the different types of categorical data often encountered in datasets. We will explore ordinal encoding in-depth and …
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Every industry uses data to make smarter decisions. But raw data can be messy and hard to understand. EDA allows you to explore and understand your data better. In this article, we’ll walk you through the basics of EDA with simple steps and examples to make it easy to follow. What is Exploratory Data Analysis? …
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Building machine learning projects using real-world datasets is an effective way to apply what you’ve learned. Working with real-world datasets will help you learn a great deal about cleaning and analyzing messy data, handling class imbalance, and much more. But to build truly helpful machine learning models, it’s also important to go beyond training and …
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Sample language model responses to different varieties of English and native speaker reactions. ChatGPT does amazingly well at communicating with people in English. But whose English? Only 15% of ChatGPT users are from the US, where Standard American English is the default. But the model is also commonly used in countries and communities where people …
Read more “Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination”
Feature engineering helps make models work better. It involves selecting and modifying data to improve predictions. This article explains feature engineering and how to use it to get better results. What is Feature Engineering? Raw data is often messy and not ready for predictions. Features are important details in your data. They help the model …
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