Logistic Regression
Logistic regression is a classification algorithm that uses a sigmoid function to model the probability of a binary outcome. Despite its name, it is a classification method rather than a regression technique.
Understanding Logistic Regression
Logistic regression is a supervised learning algorithm used for binary classification that models the probability of an outcome using the sigmoid activation function to map linear combinations of features to values between zero and one. Despite its name, logistic regression is a classification method rather than a regression technique, and it remains one of the most widely used algorithms in machine learning due to its simplicity, interpretability, and efficiency. Applications include spam detection, credit scoring, medical diagnosis, and customer churn prediction. The model is trained by optimizing a cross-entropy loss function using gradient descent. Logistic regression provides calibrated probability estimates and allows practitioners to understand which features drive predictions through coefficient analysis. It extends naturally to multi-class problems through softmax regression and serves as the conceptual foundation for understanding neural network output layers.
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.