12 docs tagged with "supervised-learning"

Understanding recursive partitioning, Entropy, Gini Impurity, and how to prevent overfitting in tree-based models.

Combining L1 and L2 regularization for the ultimate balance in feature selection and model stability.

Exploring the power of Sequential Ensemble Learning, Gradient Descent, and popular frameworks like XGBoost and LightGBM.

Understanding the proximity-based classification algorithm: distance metrics, choosing K, and the curse of dimensionality.

Understanding L1 regularization, sparse models, and automated feature selection.

Mastering the fundamentals of predicting continuous values using lines, slopes, and intercepts.

Understanding binary classification, the Sigmoid function, and decision boundaries.

Learning to model curved relationships by transforming features into higher-degree polynomials.

Understanding Ensemble Learning, Bagging, and how Random Forests reduce variance to build robust classifiers.

Mastering L2 regularization to prevent overfitting and handle multicollinearity in regression models.

A deep dive into supervised learning: regression, classification, and the relationship between features and targets.

Mastering the geometry of classification: margins, hyperplanes, and the Kernel Trick.