Building reliable machine learning models with cross-validation

Cross-validation is a technique used to measure and evaluate machine learning models performance. During training we create a number of partitions of the training set and train/test on different subsets of those partitions. Cross-validation is frequently used to train, measure and finally select a machine learning model for a given dataset because it helps assess…

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Introducing Comet.ml’s new Query Builder

Now you can easily find and organize your experiments with filtered views based on experiment metrics, metadata, and parameters Machine learning teams often work with many, many models and their iterations (often in the hundreds and thousands 📈). We received overwhelming feedback from our users about needing improved discoverability for their machine learning experiments. To…

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Part I: Conducting Exploratory Data Analysis (EDA) for the Kaggle Home Credit Default Competition

Follow along as the Comet.ml team competes to win the Kaggle Home Credit Default Competition — this is the first of a series of posts on our modeling process! In this first post, we are going to conduct some preliminary exploratory data analysis (EDA) on the datasets provided by Home Credit for their credit default risk Kaggle competition…

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Using fastText and Comet.ml to classify relationships in Knowledge Graphs

TLDR: In this post, we will examine how a simple model, fastText, learns to represent entities in a subset of the FB15K knowledge graph, by classifying the relationship between pairs of entities in the graph. An increasing number of machine learning solutions, and companies are leveraging knowledge graph data, to tackle industries that require deep domain…

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