Untitled Quiz

A method for automating decision-making by learning from examples.

A type of machine learning where the algorithm learns from input/output pairs.

Examples used to train a supervised learning algorithm; each example contains an input and its corresponding expected output.

Identifying faces in images—a problem historically solved by hand-coding rules but now often addressed using machine learning.

A method of describing a problem to a computer by explicitly defining rules rather than learning them from examples.

Using machine learning to categorize emails as spam or not spam.

A very small dot that makes up a digital image.

An algorithm that learns from input/output pairs. A 'teacher' or programmer provides known answers for the input.

Collecting input/output pairs for training the algorithm, often requiring specific methods and resources.

A supervised learning task where the input is a handwritten digit image, and the output is the actual digit.

A supervised learning task where the input is a medical image, and the output is whether a tumor is benign or malignant.

A supervised learning task where the input is a credit card transaction record, and the output is whether it's fraudulent.

Collecting credit card transactions and their corresponding fraud labels, usually by recording customer reports.

A model's ability to accurately predict on unseen data, similar to its training data, indicating its effectiveness.

A model that performs exceptionally well on training data but poorly on new data. It 'memorizes' the training data instead of learning the underlying patterns.

A model that fails to learn the underlying patterns in the data and performs poorly, both on training and new data. It's too simplistic.

The data set used to train a machine learning model. It provides examples for the model to learn from.

New, unseen data used to evaluate the model's performance. It assesses how well the model generalizes to new situations.

A classification algorithm that predicts the class of a new data point by considering its closest neighbors in the training dataset. It determines the class based on the majority vote among those neighbors.

The closest data points in the training dataset to a new data point for which we want to make a prediction.

A simplified version of the k-NN algorithm where the prediction is based on the closest single data point in the training dataset.

The number of nearest neighbors considered in the k-Nearest Neighbors algorithm.

In the k-NN algorithm, the class label assigned to a new data point is determined by the class that has the most votes among its k nearest neighbors.

A classification task where there are only two possible classes.

A classification task where there are more than two possible classes.

A popular Python library that provides tools for machine learning, including the k-Nearest Neighbors algorithm.

Dividing data into training and testing sets. The training set is for model learning, while the testing set evaluates the learned model's performance on unseen data.

A machine learning algorithm that classifies data points based on their k-nearest neighbors in the training data. The majority class among the k-nearest neighbors determines the predicted class of a new data point.

Training the machine learning algorithm by providing it with the training data. The algorithm learns the patterns and relationships in the data to improve its predictive capability.

Using a trained model to make a prediction about a new data point based on the patterns the model learned from the training data.

A metric used to evaluate the performance of a machine learning model. It indicates the percentage of correct predictions made by the model on the testing data.

A line or region that separates different classes in a classification model. In a KNN model, the decision boundary is formed by the votes of neighboring points.

The complexity of a KNN model is determined by the number of neighbors ('k') used for classification. Fewer neighbors (high complexity) result in more detailed decision boundaries, while more neighbors (low complexity) create smoother boundaries.

When a KNN model uses too few neighbors (high complexity), it can become too sensitive to the training data, leading to poor performance on unseen data. This is called overfitting.

When a KNN model uses too many neighbors (low complexity), it might not capture the subtle patterns in the data, resulting in poor performance on both training and unseen data. This is called underfitting.

A model's generalization ability refers to how well it performs on new, unseen data. The complexity of a KNN model (number of neighbors) influences its generalization. Models that are too complex (overfit) may perform poorly on unseen data, while models that are too simple (underfit) may not adequately capture the patterns in the data.

To evaluate a model's performance, we split the data into two sets: training data for learning and test data for evaluating the model's ability to generalize to unseen data.

To understand how well a KNN model performs, we evaluate it on the test set. This helps us determine if the model is overfitting or underfitting by comparing its performance on training and test data.

If a KNN model uses all data points as neighbors, all predictions will simply be the class that is most frequent in the training set. The model will be highly simplified and unable to capture any specific patterns.