F1 Score
The F1 score is the harmonic mean of precision and recall, providing a single metric that balances both. An F1 score of 1 indicates perfect precision and recall, while 0 indicates total failure.
Understanding F1 Score
The F1 score is an evaluation metric that computes the harmonic mean of precision and recall, providing a single number that balances both concerns. It is particularly valuable for classification tasks with imbalanced datasets where accuracy alone can be misleading. An F1 score of 1.0 indicates perfect precision and recall, while 0.0 is the worst possible score. Variants include the macro F1, which averages scores across all classes equally, and the weighted F1, which accounts for class frequency. The F1 score is widely used in natural language processing tasks like named entity recognition and text classification, as well as in medical diagnosis and fraud detection systems. During cross-validation and hyperparameter tuning, the F1 score often serves as the optimization target to ensure models perform well across both positive and negative predictions.
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.