Discover the power of t-SNE for visualizing high-dimensional data, with a step-by-step Python implementation and detailed explanations.
To train robust machine learning models, large data sets with many dimensions are required to recognize sufficient structures and provide the best possible predictions. However, such multi-dimensional data is difficult to visualize and understand. Therefore, dimension reduction methods are needed to visualize complex data structures and perform analysis.
t-Distributed Stochastic Neighbor Embedding (t-SNE/tSNE) is a dimension reduction method that relies on the distances between data points and attempts to keep these distances in lower dimensions. It is a method from the field of unsupervised learning and is also capable of separating non-linear data, that is, data that cannot be divided by a line.
Various algorithms, such as linear regressionyou have problems if the dataset contains variables that are correlatedThat is, they depend on each other. To avoid this problem, it may make sense to remove from the data set the variables that are correlated…
