Gradient Boosting is another ensemble learning technique used for classification and regression tasks and has its own specific way of building the ensemble of weak learners. Here’s a brief overview of Gradient Boosting: Gradient Boosting typically produces more accurate models compared to AdaBoost but can be more computationally expensive and prone to overfitting, especially with…
AdaBoost (Adaptive Boosting) is a popular ensemble learning algorithm used for classification and regression tasks. It works by combining multiple weak learners (typically decision trees, often referred to as “stumps”) to create a strong learner. Here’s how it generally works: AdaBoost is effective because it focuses on improving the classification of difficult examples by giving…
The BaggingClassifier is an ensemble meta-estimator in machine learning, belonging to the bagging family of methods. Bagging stands for Bootstrap Aggregating. The main idea behind bagging is to reduce variance by averaging the predictions of multiple base estimators trained on different subsets of the training data. Here’s how the BaggingClassifier works: The BaggingClassifier in scikit-learn…
In the context of the train_test_split function in machine learning, the stratify parameter is used to ensure that the splitting process preserves the proportion of classes in the target variable. When you set stratify=y, where y is your target variable, the data is split in a way that maintains the distribution of classes in both…
t-SNE, which stands for t-distributed Stochastic Neighbor Embedding, is a popular dimensionality reduction technique (of type Feature Extraction) used in machine learning and data visualization. It is particularly useful for visualizing high-dimensional data in a lower-dimensional space, typically two or three dimensions, while preserving the local structure of the data as much as possible. The…
Principal Component Analysis (PCA) is a widely used linear dimensionality reduction technique (of type Feature Extraction) used for reducing the dimensionality of datasets containing many correlated variables while preserving most of the variability in the data. Here’s how PCA works: Each of the “new” variables after PCA are all independent of one another. PCA has…
Feature Elimination and Feature Extraction are two common techniques used in dimensionality reduction, a process aimed at reducing the number of features (or dimensions) in a dataset while preserving the most important information. Both techniques are used to address the curse of dimensionality, improve computational efficiency, and potentially enhance model performance. However, they differ in…
he cophenetic coefficient is a measure used to evaluate the quality of a hierarchical clustering solution. It quantifies how faithfully the hierarchical structure (dendrogram) preserves the original pairwise distances or dissimilarities between data points. Here’s how it works: A high cophenetic coefficient suggests that the hierarchical clustering solution accurately captures the underlying structure of the…
omplete linkage hierarchical clustering is another method used in cluster analysis, like single linkage clustering, but with a different approach to determining the distance between clusters. In complete linkage clustering, the distance between two clusters is defined as the maximum distance between any two points in the two clusters. So, the distance between two clusters…
ingle linkage hierarchical clustering is a method used in cluster analysis to group similar data points into clusters based on their proximity or similarity. It is a bottom-up approach, starting with each data point as its own cluster and then iteratively merging the closest pairs of clusters until only one cluster remains. In single linkage…
