Mastering ML Datasets: The Key to Building Smarter Models Data are the lifeblood of ML. The success of algorithms in AI depends on the quality of data selected for training and testing purposes. The very fact that, unless trained with reliable quality data, the chances of an ML model being successful are slim brings home the importance of fairness, relevance, balanced data distribution, and richness of datasets. This blog seeks to shine a light on the crucial role of ml datasets ; it also looks at the different types of datasets that may be applied in the AI environment and discusses the recommended best practices for obtaining, preparing, and employing data for building better models. The Importance of ML Datasets Predictive accuracy of any algorithm is heavily reliant on the dataset that feeds it. Using poor quality datasets with modern algorithms does little to yield meaningful results. For this reason, datasets serve a vital role in: Model Training: An array of supervised learnin...
