Ensemble Classifier | Data Mining
Ensemble learning helps improve machine learning results by combining several models. This approach allows the production of better predictive performance compared to a single model.… Read More »
Ensemble learning helps improve machine learning results by combining several models. This approach allows the production of better predictive performance compared to a single model.… Read More »
In the real-world applications of machine learning, it is very common that there are many relevant features available for learning but only a small subset… Read More »
Null Space and Nullity are concepts in linear algebra which are used to identify the linear relationship among attributes. Null Space: The null space of… Read More »
Prerequisite: K-means clustering The internet is filled with huge amounts of data in the form of images. People upload millions of pictures every day on… Read More »
Prerequisite: Optimal value of K in K-Means Clustering K-means is one of the most popular clustering algorithms, mainly because of its good time performance. With… Read More »
Cluster Analysis – The aim of the clustering process is to discover overall distribution patterns and interesting correlations among the data attributes. It is the… Read More »
Real-world data tend to be noisy. Noisy data is data with a large amount of additional meaningless information in it called noise. Data cleaning (or… Read More »
Kolmogorov–Smirnov test a very efficient way to determine if two samples are significantly different from each other. It is usually used to check the uniformity… Read More »
Different performance metrics are used to evaluate different Machine Learning Algorithms. In case of classification problem, we have a variety of performance measure to evaluate… Read More »
Prerequisite: Classifying data using SVM In Linear SVM, the two classes were linearly separable, i.e a single straight line is able to classify both the… Read More »
In data mining and statistics, hierarchical clustering analysis is a method of cluster analysis which seeks to build a hierarchy of clusters i.e. tree type… Read More »
Simple Linear Regression is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables. One variable denoted x is… Read More »
R-squared is a statistical measure that represents the goodness of fit of a regression model. The ideal value for r-square is 1. The closer the… Read More »
Clustering analysis or simply Clustering is basically an Unsupervised learning method that divides the data points into a number of specific batches or groups, such… Read More »
Linear Discriminant Analysis or Normal Discriminant Analysis or Discriminant Function Analysis is a dimensionality reduction technique which is commonly used for the supervised classification problems.… Read More »