A decision tree splits data into branches based on feature values, resembling a flowchart of yes-or-no questions. They are easy…

Cross-validation assesses how well a model generalizes to unseen data by splitting the dataset into multiple training and testing subsets….

Clustering groups similar data points together without predefined labels, making it a form of unsupervised learning. Popular methods include k-means…

Transfer learning reuses a model trained on one task as a starting point for a related task, saving time and…

Model evaluation measures how well a trained model performs using metrics like accuracy, precision, and recall. The right metric depends…

Hyperparameters are settings configured before training begins, such as learning rate or tree depth. Tuning these values can significantly improve…

Batch normalization standardizes the inputs to each layer of a neural network during training. This helps stabilize and speed up…