This tutorial covers fine-tuning BERT for sentiment analysis using Hugging Face Transformers. Learn to prepare data, set up environment, train and evaluate the model, and make predictions.
This article explains the concept and use of Friedman's H-statistic for finding interactions in machine learning models.
- The H-stat is a non-parametric method that works well with ordinal variables, and it's useful when the interaction is not linear.
- The H-stat compares the average rank of the response variable for each level of the predictor variable, considering all possible pairs of levels.
- The H-stat calculates the sum of these rank differences and normalizes it by the total number of observations and the number of levels in the predictor variable.
- The lower the H-stat, the stronger the interaction effect.
- The article provides a step-by-step process for calculating the H-stat, using an example with a hypothetical dataset about the effects of asbestos exposure on lung cancer for smokers and non-smokers.
- The author also discusses the assumptions of the H-stat and its limitations, such as the need for balanced data and the inability to detect interactions between more than two variables.
The Towards Data Science team highlights recent articles on the rise of open-source LLMs, ethical considerations with chatbots, potential manipulation of LLM recommendations, and techniques for temperature scaling and re-ranking in generative AI.
This article describes how to use GNU Emacs for quick data visualization in combination with Gnuplot. It provides a command that can be used to visualize the correlation of data without needing any setup or specific files. The article also includes an example of a command for generating a graph using a data range selected with a rectangle command copy-rectangle.
This article provides a comprehensive guide to performing exploratory data analysis on time series data, with a focus on feature engineering.
- Contextual bandits, a dynamic approach to treatment personalization
- Differences between contextual bandits, multi-armed bandits, A/B testing, multiple MABs, multi-step reinforcement learning, and uplift modeling
- Exploration and exploitation strategies, including ε-greedy, upper confidence bound, and Thompson sampling
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