16 docs tagged with "classification"

Understanding the most common evaluation metric, its formula, and its fatal flaws in imbalanced datasets.

Hands-on machine learning projects for beginners, including house price prediction, iris classification, and customer segmentation.

Understanding the foundations of binary outcomes: The Bernoulli trial and the Binomial distribution, essential for classification models.

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

Mastering the harmonic mean of Precision and Recall to evaluate models on imbalanced datasets.

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 cross-entropy loss and why it is the gold standard for evaluating probability-based classifiers.

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

Understanding Precision, its mathematical foundation, and why it is vital for minimizing False Positives.

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

Understanding Recall, its mathematical definition, and why it is critical for minimizing False Negatives.

Evaluating classifier performance across all thresholds using the Receiver Operating Characteristic and Area Under the Curve.

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.

The foundation of classification evaluation: True Positives, False Positives, True Negatives, and False Negatives.