Cross-Entropy
Cross-entropy is a loss function that measures the difference between two probability distributions — typically the model's predictions and the true labels. It is the standard loss function for classification tasks in deep learning.
Understanding Cross-Entropy
Cross-entropy is a loss function widely used in classification tasks that measures the divergence between predicted probability distributions and the true labels. When a model outputs a probability via a softmax activation function, cross-entropy penalizes confident but wrong predictions more heavily than uncertain ones, making it an effective training signal. It is the standard loss for tasks like image classification with convolutional neural networks, text classification in natural language processing, and multi-class problems across deep learning. Cross-entropy is closely related to concepts from information theory such as entropy and KL divergence. Minimizing cross-entropy through gradient descent effectively pushes a model's predicted distribution closer to the ground truth, improving both accuracy and calibration of predictions over successive epochs.
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.