If K=4 and there are 200 points, on average, each cluster will have 200/4 = 50 points assigned to it.

The method that defines the distance between two clusters as the maximum distance between points in the clusters is called complete linkage.

In K-means clustering, the number of centroids initialized is equal to the number of clusters. Therefore, if there are 5 clusters, 5 centroids will be initialized.

Agglomerative hierarchical clustering starts with each data point as its own cluster and merges them into larger clusters based on similarity.

Agglomerative clustering begins with each data point as its own cluster and merges them iteratively based on distance until a single cluster is formed.

Agglomerative clustering begins with each data point as its own cluster and progressively merges them based on their similarities.

A dendrogram is a tree-like diagram that illustrates the arrangement of clusters formed during hierarchical clustering.

Linkage in hierarchical clustering refers to the strategy used to determine the distance between clusters, which affects how clusters are merged.

Agglomerative clustering is a bottom-up approach where each data point starts as its own cluster and pairs of clusters are merged as one moves up the hierarchy.

Agglomerative clustering starts with individual points and merges them into clusters, while divisive clustering starts with one cluster and splits it into smaller clusters.

A dendrogram visually represents the arrangement of clusters and the distances at which they are merged.

The agglomerative approach in hierarchical clustering starts with individual data points and merges them into larger clusters based on similarity.

If K is set too high, the model may overfit the data, resulting in too many clusters that do not generalize well.

Hierarchical clustering is preferred when a hierarchy of clusters is desired, as it provides a tree-like structure of the data.

Reinforcement learning is used when a model learns from interactions with an environment, optimizing actions based on rewards.

Feature engineering involves creating new input features from existing data to improve model performance.

Clustering is often used in marketing to segment customers into distinct groups based on purchasing behavior or demographics.

Customer segmentation is a common application of clustering, where businesses group customers based on purchasing behavior or demographics.

K-means clustering is often used in image segmentation to group similar pixels together.

Hierarchical clustering does not require the number of clusters to be predetermined, allowing for more flexibility in exploring data.

DBSCAN can identify clusters of arbitrary shape and does not require the number of clusters to be specified in advance.

Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize to new data.

Outliers can significantly distort the cluster centroids in K-means clustering, leading to inaccurate clustering results.

Hierarchical clustering does not require the number of clusters to be predetermined, allowing for more flexibility in analysis.

Hierarchical clustering does not require the number of clusters to be specified in advance, allowing for more flexibility in cluster formation.

Both the Silhouette score and the Elbow method are commonly used criteria for determining the optimal number of clusters in K-means clustering.

K-means is a partitional clustering method that divides data into a fixed number of clusters, while hierarchical clustering builds a tree of clusters without needing to specify the number of clusters in advance.

K-means uses centroids to represent clusters, while K-medoids uses actual data points as the center of clusters, making it more robust to outliers.

The main difference is that supervised learning uses labeled data for training, while unsupervised learning works with unlabeled data.

Activation functions introduce non-linearity into neural networks, allowing them to learn complex patterns.