Underfitting
Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test data. Increasing model complexity or training longer can address underfitting.
Understanding Underfitting
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data, resulting in poor performance on both training and test sets. It represents the high-bias end of the bias-variance tradeoff, where the model's assumptions are too restrictive to learn the true data relationships. Common causes include insufficient model complexity, excessive regularization, too few training epochs, or inadequate feature representation. For example, fitting a linear model to data with complex nonlinear relationships will underfit. Solutions include increasing model capacity by adding layers or parameters, training for more epochs, engineering better features, reducing regularization strength, or choosing a more expressive architecture. Detecting underfitting is typically straightforward since training metrics themselves will be poor, unlike overfitting where training performance appears deceptively strong.
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.