In KNN, what happens when yo increase the value of K?Answer areaThe model becomes more complex and prone to overfittingThe model becomes simpler and less prone to overfittingThe computational cost decreasesThe distance between neighbors increases

What happens to a k-NN model as you increase the value of k? (Select One correct answer)

What happens to a k-NN model as you increase the value of k? (Select One correct answer)A. The variance increasesB. The bias increases.

What are the disadvantages of using the KNN algorithm?1 pointAs the number of dimensions increases, the distance between any two points in the space becomes increasingly large, making it difficult to find meaningful nearest neighbors.Computationally expensive, especially for large datasets, and requires a large amount of memory to store the entire dataset.Sensitive to the choice of K and distance metric.All of the aboveHow do you choose the value of K (the number of neighbors to consider) in the KNN algorithm? (Select 2)1 pointA small value of K, for example, K=1, will result in a more flexible model but may be prone to overfitting.A large value of K, for example, K=n, where n is the size of the dataset, will result in a more stable model but may not capture the local variations in the data.A large value of K, for example, K=n, where n is the size of the dataset, will result in a more flexible model but may be prone to overfitting.A small value of K, for example, K=1, will result in a more stable model but may not capture the local variations in the data.How do you handle imbalanced data in the KNN algorithm?1 pointWeighted voting, where the vote of each neighbor is weighted by its inverse distance to the query point. This gives more weight to the closer neighbors and less weight to the farther neighbors, which can help to reduce the effect of the majority class.Oversample the minority class.Undersample the majority class.All of the above.What are the ideal use cases for KNN?1 pointKNN is best suited for small to medium-sized datasets with relatively low dimensionality. It can be useful in situations where the decision boundary is linear. It can be effective in cases where the data is clustered or has distinct groups.KNN is best suited for large datasets with relatively high dimensionality. It can be useful when the decision boundary is highly irregular or nonlinear. It can be effective in cases where the data is clustered or has distinct groups.KNN is best suited for small to medium-sized datasets with relatively low dimensionality. It can be useful when the decision boundary is highly irregular or nonlinear. It can be effective in cases where the data is clustered or has distinct groups.KNN is best suited for small to medium-sized datasets with relatively low dimensionality. It can be useful when the decision boundary is highly irregular or nonlinear. It can be effective in cases where the data is not clustered or doesn’t have distinct groups.Select the correct statements about the K-means algorithm. (Select 2)1 pointK-means is an unsupervised learning algorithmK-means is a clustering algorithmK-means is a supervised learning algorithmK-means is a classification algorithmSelect the correct statement about the applications of K-means clustering? (Select 2)1 pointIn marketing, K-Means can help segment customers into different groups based on their behavior, preferences, or purchase patterns. (Customer segmentation)K-Means can be employed for image compression by clustering similar colors together and reducing the color palette without significant loss of visual quality. (Image compression)By considering outliers as anomalies, K-Means can help identify unusual or suspicious data points. (Anomaly detection)All of the above.K stands for the number of clusters in K-Means. Which methods can be used to find the optimal value of K in K-means clustering? 1 pointElbow methodSilhouette methodUMAPDBSCANWhat are the advantages of K-Means clustering? 1 pointK-Means scales well to large datasets and is computationally efficient, making it suitable for many applications.K-Means scales well to small datasets and is computationally efficient, making it suitable for many applications.The resulting clusters in K-Means are easy to interpret since each data point belongs to a specific cluster. It provides meaningful insights into the structure of the data.The resulting clusters in K-Means are easy to interpret since each data point belongs to multiple clusters. It provides meaningful insights into the structure of the data.Which statement is correct about the drawbacks of K-Means clustering?1 pointK-Means is sensitive to the initial placement of centroids. Depending on the initial centroids and data distribution, K-Means may converge to a local optimum rather than the globally optimal solution.You need to specify the number of clusters (K) in advance. Determining the optimal number of clusters can be challenging and may require domain knowledge or additional techniques.Outliers can significantly impact the clustering result. K-Means tends to assign outliers to the nearest cluster, even if they don’t belong to any specific cluster.All of the above.

When applying k-Nearest Neighbors (KNN) for classification, what is the role of the "k" parameter?a.It determines the learning rate in the algorithm.b.It sets the number of nearest neighbors to consider for classification.c.It specifies the number of dimensions in the dataset.d.It defines the number of clusters.

In K Nearest Neighbours classification, which of the following statements about K and N (the number of training samples) is/are true?Group of answer choicesK impacts both the accuracy and the computational complexity of the KNN algorithm.K and N should be linearly related, i.e. K/N should be a pre-determined constant.The time taken to perform the classification task increases with N.K should be odd to avoid ties.