Classification
Classification is a supervised learning task where the model predicts which category or class an input belongs to. Examples include email spam detection, image recognition, and sentiment analysis.
Understanding Classification
Classification is a cornerstone supervised learning task where models assign input data to predefined categories based on patterns discovered in labeled training examples. The scope spans from binary classification with two categories to multi-class problems with hundreds of labels and multi-label scenarios where inputs belong to multiple categories simultaneously. Real-world classification powers spam detection, medical diagnosis, image recognition through convolutional neural networks, and sentiment analysis using transformer models like BERT. The choice of algorithm depends on data characteristics: logistic regression and decision trees work well for interpretable models on structured data, while deep learning excels with unstructured inputs like images and text. Model evaluation employs metrics derived from the confusion matrix, including precision, recall, F1 score, and accuracy. Effective classification requires careful attention to data quality, class imbalance, feature engineering, and the bias-variance tradeoff.
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.