Intermediate
What is self-supervised learning?
Self-supervised learning is a machine learning approach that creates training signals from unlabeled data itself. It converts aspects of an unsupervised problem into supervised learning by generating surrogate labels, making it useful when labeled data is scarce but unlabeled data is abundant.
What is semi-supervised learning?
Semi-supervised learning is a machine learning approach that uses both labeled and unlabeled data during training. It is useful when labeled examples are costly or limited but larger volumes of unlabeled data can help improve representation, classification, or prediction.
What is training data?
Training data is the dataset used to teach or fit a machine learning model by adjusting its learnable parameters. Its quality, quantity, diversity, labeling, and provenance strongly influence the performance, reliability, and compliance posture of the resulting model.
What is overfitting?
Overfitting occurs when a model learns the training data too closely, including noise or idiosyncratic details, and performs poorly on new data. It is commonly indicated by high training performance but weaker validation or test performance.
What is underfitting?
Underfitting occurs when a model is too simple or insufficiently trained to capture meaningful patterns in the data. An underfit model performs poorly on both training data and new data because it has not learned the underlying relationships needed for the task.
What is a decision tree?
A decision tree is an interpretable supervised learning model that represents decision rules in a tree-like structure. Inference follows a path from a root node through feature-based conditions to a leaf node that provides the predicted class or value.
Advanced
What is deep learning?
Deep learning is a subfield of machine learning that uses neural networks with multiple layers to learn representations and patterns from data.
How are classification and regression different?
Use Classification when the target is a category; use Regression when the target is a continuous or numerical value.