XGBoost
XGBoost (Extreme Gradient Boosting) is a highly optimized gradient boosting library known for its speed and performance in structured data competitions. It remains one of the most popular algorithms for tabular data.
Understanding XGBoost
XGBoost (Extreme Gradient Boosting) is a highly optimized implementation of the gradient boosting ensemble method that has become one of the most successful algorithms for structured and tabular data. It builds an ensemble of decision trees sequentially, with each new tree correcting the residual errors of the previous ones, using regularization techniques to prevent overfitting. XGBoost dominated machine learning competitions on platforms like Kaggle for years, winning numerous prizes across diverse problem domains including click-through rate prediction, fraud detection, and customer churn modeling. Its key innovations include a sparsity-aware algorithm for handling missing values, weighted quantile sketch for approximate tree learning, and built-in support for parallel and distributed computing. While deep learning excels on unstructured data like images and text, XGBoost remains the go-to algorithm for supervised learning on tabular datasets in production environments.
Category
Machine Learning
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Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.