The loss function quantifies how well the neural network's predictions match the actual target values.

The output layer generates the final predictions of the neural network based on the processed information from previous layers.

Bootstrapping involves creating multiple subsets of the training data, which helps in building diverse trees in the Random Forest.

Using multiple Decision Trees in a Random Forest helps to reduce overfitting and improve the overall accuracy of the model.

SVM is widely used in image recognition tasks, where it can classify images based on their features.

Linear regression is best suited for predicting continuous outcomes, such as house prices based on various features.

Linear regression is suitable for predicting continuous outcomes, such as house prices based on various features.

R-squared is a common metric for evaluating the goodness of fit in regression models, indicating how well the model explains the variability of the data.

R-squared indicates the strength of the relationship between the independent variables and the dependent variable in regression.

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern, leading to poor performance on new data.

Cross-validation is used to evaluate the model's performance on unseen data by partitioning the dataset into training and validation sets.

The F1 Score is the harmonic mean of precision and recall, providing a balance between the two metrics.

The class label refers to the predicted output category in classification problems.

Precision measures the ratio of true positives to the total predicted positives, indicating the accuracy of positive predictions.

The F1 Score is the harmonic mean of precision and recall, providing a balance between the two metrics.

'Epsilon' defines the maximum distance between two points for them to be considered part of the same cluster in DBSCAN.

Gini impurity measures the impurity or disorder of a node, helping to determine the best split at each node.

Silhouette Score measures how similar an object is to its own cluster compared to other clusters.

One-hot encoding transforms categorical variables into a binary matrix, making them suitable for machine learning algorithms.

Normalization is the process of scaling features to a specific range, often [0, 1].

Neural networks are effective in fraud detection, analyzing transaction patterns to identify potentially fraudulent activities.

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 visually represents the arrangement of clusters in hierarchical clustering.

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.