A supervised learning task where the goal is to assign labels to input data based on learned patterns.

A widely used dataset containing images of handwritten digits, essential for training classification models.

k-Nearest Neighbors, a simple algorithm that classifies data points based on the majority label of their nearest neighbors.

Support Vector Machine, a classification algorithm that finds the optimal hyperplane to separate different classes.

The process of selecting, modifying, or creating features to improve model performance.

The process of optimizing algorithm parameters to enhance model accuracy.

A technique to assess how a model's results generalize to an independent dataset.

A table used to evaluate the performance of a classification model by comparing predicted and actual labels.

Receiver Operating Characteristic curve, a graphical representation of a model's performance at various threshold settings.

The process of assessing a model's performance using various metrics to ensure reliability.

The technique of scaling data to fit within a specific range, improving model training.

Steps taken to clean and prepare raw data for analysis, crucial for effective modeling.

The process of applying a specific algorithm to a dataset to create a model.

Choosing the most appropriate algorithm or model based on the data and task requirements.

Measures used to determine the similarity or dissimilarity between data points in k-NN.

A subset of data used to train a model, allowing it to learn patterns.

A separate subset of data used to evaluate the performance of a trained model.

The graphical representation of data to identify patterns or insights.

Balancing different model characteristics, such as accuracy and interpretability.

Refers to the number of parameters in a model, affecting its ability to generalize.

A set of algorithms modeled after the human brain, used for complex classification tasks.

Various fields where classification models can be applied, like finance and healthcare.

The process of extracting actionable knowledge from data analysis.

A measure of how often the model's predictions are correct.

Dividing a dataset into training and testing sets to evaluate model performance.