Crossvalidation

Introduction When it comes to building and training machine learning models, one crucial aspect that often gets overlooked is ensuring the reliability and robustness of their performance. This is where cross-validation comes into play. Cross-validation is a powerful technique used to assess and validate the performance of machine learning models. In…

In the previous article, we discussed two important concepts that we wanted to explore further when evaluating the training data and determining if the model was suitable: Overfitting and Underfitting. Let’s start by defining these terms in a more personable manner: Overfitting Overfitting occurs when our model becomes too “attached” to…

Advanced techniques for model validation and dealing with unbalanced data in credit risk modeling. — To satisfy the main hallmarks of scientific model development — rigor, testability, replicability, precision, and confidence — it is important to consider model validation and how to deal with unbalanced data. …

In the world of machine learning, we often encounter the challenge of evaluating how well a model will perform on new, unseen data. This is where K-fold cross validation comes into play. In this blog post, we’ll break down the concept of K-fold cross validation using simple terms, provide intuitive…

Validating our model is one of the crucial steps in selecting the best-performing one. Usually, we use cross-validation (CV) as our primary tool for estimating the expected risk (loss or likelihood) on future data. In 10-fold CV, we split the training dataset into 10 equally-sized parts, then train on 9…

During your time working with ML models, you might have had a scenario where your machine learning model was well tested, and you achieved good accuracy with it. Being pleased by the work you’ve done, you decided to deploy. However, when the actual data was applied to the model in…

In this article,I will explain you what Cross-Validation is and techniques available to do Cross-Validation in machine learning. Cross-Validation: Cross-Validation is a widely used technique in machine learning to assess or validate the performance of the models. The following are some commonly used cross-validation techniques: K-Fold Cross-Validation: In this method, the…

Every ML Enthusiast, when they are working on a data sample that is too small, to improve the accuracy of the model, their go-to technique will be cross-validation. Cross Validation resampling technique is a quality indicator of any data-derived quantity. It uses different data portions to test and train a…

A Python implementation of cross-validation for boosted sample data to prevent data leakage and overestimating your model’s performance — This story introduces cross-validation for boosted (upsampled) sampling strategy data and an implementation in Python. Boosted sampling is often used to target the minority class labels in (highly) imbalanced datasets. …