Category: Machine Learning Miscellaneous

Cross-validation is a powerful technique used in machine learning to estimate the performance of a model on unseen data. It is an essential step in building a robust machine learning model, as it helps to identify overfitting or underfitting, and helps to determine the optimal model hyperparameters. What is Cross-Validation? Cross-validation is a technique used…

Bagging is an ensemble learning technique that combines the predictions of multiple models to improve the accuracy and stability of a single model. It involves creating multiple subsets of the training data by randomly sampling with replacement. Each subset is then used to train a separate model, and the final prediction is made by averaging…

Gradient Boosting Machines (GBM) is a powerful machine learning technique that is widely used for building predictive models. It is a type of ensemble method that combines the predictions of multiple weaker models to create a stronger and more accurate model. GBM is a popular choice for a wide range of applications, including regression, classification,…

Boosting is a popular ensemble learning technique that combines several weak learners to create a strong learner. It works by iteratively training weak learners on subsets of the data and assigning higher weights to the misclassified samples to increase their importance in the subsequent iterations. This process is repeated until the desired level of performance…

Introduction In order to execute and produce results successfully, a machine learning model must automate some standard workflows. The process of automate these standard workflows can be done with the help of Scikit-learn Pipelines. From a data scientist’s perspective, pipeline is a generalized, but very important concept. It basically allows data flow from its raw…

Performance metrics in machine learning are used to evaluate the performance of a machine learning model. These metrics provide quantitative measures to assess how well a model is performing and to compare the performance of different models. Performance metrics are important because they help us understand how well our model is performing and whether it…