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.
Understanding Accuracy
Accuracy serves as one of the most intuitive evaluation metrics in machine learning, representing the percentage of predictions a model gets right out of all predictions made. However, relying solely on accuracy can be dangerously misleading, especially in scenarios involving imbalanced datasets. For instance, a fraud detection model that labels every transaction as legitimate could achieve 99% accuracy if only 1% of transactions are fraudulent, yet it would catch zero actual fraud cases. This is why practitioners pair accuracy with complementary metrics such as precision, recall, and the confusion matrix to gain a fuller picture of model performance. In binary classification and multi-class classification tasks alike, understanding when accuracy is a reliable indicator versus when it masks poor predictions is a critical skill for any data scientist or machine learning engineer.
Category
Machine Learning
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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.
Boosting
Boosting is an ensemble method that trains models sequentially, with each new model focusing on correcting the errors of previous ones. Popular boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.