Author: Tayyaba Syed

Data scaling is a pre-processing technique used in Machine Learning to normalize or standardize the range or distribution of features in the data. Data scaling is essential because the different features in the data may have different scales, and some algorithms may not work well with such data. By scaling the data, we can ensure…

Grid Search is a hyperparameter tuning technique in Machine Learning that helps to find the best combination of hyperparameters for a given model. It works by defining a grid of hyperparameters and then training the model with all the possible combinations of hyperparameters to find the best performing set. In other words, Grid Search is…

The AUC-ROC curve is a commonly used performance metric in machine learning that is used to evaluate the performance of binary classification models. It is a plot of the true positive rate (TPR) against the false positive rate (FPR) at different threshold values. What is the AUC-ROC Curve? The AUC-ROC curve is a graphical representation…

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…