Question 2Which one of the following statements is most accurate? 1 pointLinear regression needs to remember the entire training dataset in order to make a prediction for a new data sample. KNN determines which points are closest to a given data point, so it doesn’t take long to actually perform prediction. K nearest neighbors (KNN) needs to remember the entire training dataset in order to classify a new data sample. KNN only needs to remember the hyperplane coefficients to classify a new data sample.

Question 3Which one of the following statements is most accurate about K Nearest Neighbors (KNN)? 1 pointKNN is a classification model.KNN is an unsupervised learning method. KNN is a regression model.KNN can be used for both classification and regression.

Which one of the following statements is true regarding K Nearest Neighbors?1 pointFor high dimensional data, the best distance measure to use for KNN is the Euclidean distance.The Manhattan distance between two data points is the square root of the sum of the squares of the differences between the individual feature values of the data points.The distance between two data points is independent of the scale of their features.K Nearest Neighbors (KNN) assumes that points which are close together are similar.

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.

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.

kNN techniques are computationally efficient in the “prediction” phase, but take a long time to train.