Outliers—those rare, exceptional data points that deviate from the majority—can be both a curse and a blessing in data science. While they can disrupt model training, they can also reveal valuable insights, such as fraud, system failures, or rare behaviors. One of the most frequent questions analysts and machine learning practitioners ask is: Which algorithm … Read more
Outliers and anomalies are data points that differ significantly from the majority of a dataset. They can be the result of variability, errors, or rare events—and they can have a significant impact on the performance of machine learning models, especially those sensitive to extreme values. So, what are the 5 ways to detect outliers and … Read more
When working with real-world data, outliers are inevitable. These are data points that deviate significantly from the rest of the dataset, and they can heavily influence the performance of machine learning algorithms. If you’ve been wondering, “Which algorithm is sensitive to outliers?”, this comprehensive guide is for you. Understanding which algorithms are robust and which … Read more
In the ever-growing world of ensemble machine learning algorithms, two names often come up: AdaBoost and Gradient Boosting. Both are boosting algorithms that build strong models by combining multiple weak learners. But if you’re wondering, “Is AdaBoost better than Gradient Boosting?”, the answer depends on your specific use case, data characteristics, and performance needs. In … Read more
If you’ve been diving into machine learning, especially ensemble methods, you might be wondering: Is AdaBoost bagging or boosting? It’s a great question because understanding this distinction helps you pick the right algorithm for your problem. While both bagging and boosting fall under the umbrella of ensemble learning, they work in fundamentally different ways. In … Read more
XGBoost is often celebrated as one of the most powerful machine learning algorithms out there, especially in structured data competitions and real-world tasks. Its predictive power, flexibility, and efficiency have made it a favorite among data scientists. But is it perfect? Not quite. In this article, we’ll take a close look at the downsides of … Read more
When you’re working on a regression problem and want your predictions to be spot-on, it’s easy to get overwhelmed by all the machine learning algorithms out there. One name that pops up a lot is XGBoost. But is it actually any good for regression tasks? In this guide, we’ll explore why XGBoost is so popular, … Read more
When evaluating a classification model, one of the most commonly used metrics is ROC AUC (Receiver Operating Characteristic – Area Under the Curve). This metric measures how well a model distinguishes between positive and negative classes. However, many data scientists and machine learning practitioners ask the question: What is a good ROC AUC score? In … Read more
The MNIST dataset has long been a go-to resource for beginners venturing into machine learning and deep learning. Containing 70,000 labeled images of handwritten digits from 0 to 9, this dataset serves as a standard benchmark for image classification tasks. If you’re using PyTorch—a popular deep learning framework—loading and processing the MNIST dataset becomes both … Read more
When evaluating the performance of classification models, especially in imbalanced datasets, two of the most widely used metrics are ROC AUC (Receiver Operating Characteristic – Area Under the Curve) and PR AUC (Precision-Recall Area Under the Curve). Both metrics measure how well a model distinguishes between positive and negative classes, but they serve different purposes. … Read more
