CSDS 391 Intro to AI: Learning from Examples

True (A)

credit risk data

<p>uncertain</p> Signup and view all the answers

<p>Learning from Examples = Utilizing data to improve predictions Classifying Uncertain Data = Sorting data into categories despite uncertainty Predicting Credit Risk = Assessing the likelihood of default on loans AI = Simulating human intelligence in machines</p> Signup and view all the answers

<p>It does not require an explicit model. (D)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>4.85%</p> Signup and view all the answers

<p>Euclidean distance</p> Signup and view all the answers

<p>K-means = A clustering algorithm that partitions data into k clusters. Leave-one-out = A method for validating the performance of a model. Euclidean distance = A distance metric calculated by the square root of summing squared differences. 7-nearest neighbor = A classification approach that uses the closest 7 examples for decision-making.</p> Signup and view all the answers

<p>Clustering requires prior knowledge of class labels. (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>Because it automates the classification process based on data features.</p> Signup and view all the answers

<p>It looks at k-nearest neighbors and chooses the most frequent class. (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>A distance metric that is insensitive to small deviations in position, scale, or rotation.</p> Signup and view all the answers

<p>test</p> Signup and view all the answers

<p>It can become computationally expensive. (D)</p> Signup and view all the answers

<p>K-means = Partitions data into k clusters based on distance to the centroid. Hierarchical clustering = Creates a tree-like structure of clusters. DBSCAN = Clusters based on density of data points. Mean-shift = Finds modes of the data distribution.</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>To accurately measure the similarity or dissimilarity between data points.</p> Signup and view all the answers

<p>Minimizing the misclassification error (D)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>p(C3 |x) = p(C2 |x)</p> Signup and view all the answers

<p>class</p> Signup and view all the answers

<p>Euclidean distance = Measuring straight-line distance in multi-dimensional space Manhattan distance = Calculating distance along axes at right angles Minkowski distance = Generalization of Euclidean and Manhattan distances Cosine similarity = Determining the angle between two vectors</p> Signup and view all the answers

<p>Probability of feature x given class C2 (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Hierarchical clustering</p> Signup and view all the answers

Data used to predict if someone will default on a loan.

Using data to forecast if someone will default on a loan.

Using past data to build models that predict future events.

Categorizing data with uncertain values for predictions.

Using AI to make decisions about loan risks based on data

A classification algorithm that predicts the class of a data point based on the majority class of its k nearest neighbors in the feature space.

A measure of distance between two points in a multi-dimensional space, calculated as the square root of the sum of squared differences between corresponding coordinates.

A type of classifier that directly predicts the class of a data point based on the class label of its nearest neighbor in the feature space.

A technique used in computer vision to find instances of a specific pattern (template) within an image by calculating the similarity between the template and different regions of the image.

Any function that measures the distance or dissimilarity between two objects or data points in a feature space. This can be used to implement algorithms like nearest neighbor classification.

The ability of a distance metric to be influenced by small changes or variations (deviations) in the data, such as shifts in position or scale.

The issue of designing a distance metric that is insensitive to minor changes (deviations) in the data, such as shifts in position, scale, or rotation, but sensitive to meaningful differences.

Algorithms that learn a mapping from inputs to outputs, specifically for categorizing data points into predefined classes.

A classification algorithm where a new data point is assigned to the class of its k nearest neighbors in a training dataset.

A way to estimate the generalization error of a model by training the model on all data except one example and then testing the model on the excluded example. This process is repeated for each example, and the error rate is averaged over all examples.

The k-NN algorithm is straightforward to implement because it doesn’t require training a specific model. Instead, it simply relies on calculating distances and finding the nearest neighbors.

The k-NN algorithm can handle complex class boundaries by considering the distribution of neighboring data points.

The k-NN algorithm has several advantages, including its simplicity, ability to handle complex classes, and its ability to improve with more data points. Additionally, it doesn’t require a specific model to be built.

The error bound formula for the k-NN algorithm shows that the error rate can be reduced by increasing the number of training examples (N) and by choosing an appropriate value for k, the number of neighbors.

The USPS dataset, consisting of images of handwritten digits, can be used to demonstrate the effectiveness of k-NN. The algorithm can classify a new digit image by comparing it to the nearest known digit images.

The boundary that minimizes misclassification error by dividing data points into their most likely classes. It's determined where the probabilities of belonging to two classes are equal.

The optimal decision boundary occurs when the probability of a data point belonging to class C3 equals the probability of it belonging to class C2.

A method that classifies a new data point by assigning it to the class of its nearest known data point.

It allows us to determine which known data point is closest to a new data point, suggesting that the new point is likely to belong to the same class as its closest neighbor.

Assumes that points close together in the data space are likely to belong to the same class.

There are various tie-breaking techniques, such as choosing the class with the most neighbors within a small radius around the unknown point.

It works well when data points within the same class tend to cluster together and are well separated from points belonging to other classes.