Remove Cross Validation Remove Data Preparation Remove Exploratory Data Analysis
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The AI Process

Towards AI

Data description: This step includes the following tasks: describe the dataset, including the input features and target feature(s); include summary statistics of the data and counts of any discrete or categorical features, including the target feature. Training: This step includes building the model, which may include cross-validation.

AI 81
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Predicting Heart Failure Survival with Machine Learning Models — Part II

Towards AI

That post was dedicated to an exploratory data analysis while this post is geared towards building prediction models. In our exercise, we will try to deal with this imbalance by — Using a stratified k-fold cross-validation technique to make sure our model’s aggregate metrics are not too optimistic (meaning: too good to be true!)

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Data Preparation for AI Projects Data preparation is critical in any AI project, laying the foundation for accurate and reliable model outcomes. This section explores the essential steps in preparing data for AI applications, emphasising data quality’s active role in achieving successful AI models.

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Large Language Models: A Complete Guide

Heartbeat

In this article, we will explore the essential steps involved in training LLMs, including data preparation, model selection, hyperparameter tuning, and fine-tuning. We will also discuss best practices for training LLMs, such as using transfer learning, data augmentation, and ensembling methods.