Unlock the power of t-SNE for visualizing high-dimensional data, with a step-by-step Python implementation and in-depth explanations.
If robust machine learning models are to be trained, large datasets with many dimensions are required to recognize sufficient structures and deliver the best possible predictions. However, such high-dimensional data is difficult to visualize and understand. This is why dimension reduction methods are needed to visualize complex data structures and perform an analysis.
The t-Distributed Stochastic Neighbor Embedding (t-SNE/tSNE) is a dimension reduction method that is based on distances between the data points and attempts to maintain these distances in lower dimensions. It is a method from the field of unsupervised learning and is also able to separate non-linear data, i.e. data that cannot be divided by a line.
Various algorithms, such as linear regression, have problems if the dataset contains variables that are correlated, i.e. dependent on each other. To avoid this problem, it can make sense to remove the variables from the dataset that correlate…
