Feature Extraction
Feature extraction is the process of automatically identifying and selecting the most informative representations from raw data. Deep learning models learn to extract features hierarchically, from simple edges to complex patterns.
Understanding Feature Extraction
Feature extraction is the process of transforming raw data into a set of meaningful numerical representations that a machine learning model can effectively use for prediction or classification. Unlike feature engineering, which often involves manual domain knowledge, feature extraction frequently leverages learned representations from deep learning models. For example, a pre-trained convolutional neural network can extract visual features from images by using intermediate layer outputs, a technique known as transfer learning. In natural language processing, transformer models extract contextual features through embeddings. Feature extraction is foundational in computer vision tasks like face recognition and object detection, as well as in audio classification and anomaly detection. By converting high-dimensional raw inputs into compact, informative representations, feature extraction improves model efficiency and is often a preprocessing step in a broader data pipeline.
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.