Visualizing data through graphs, charts, and other tools is crucial for understanding the complete narrative within the data. To underscore the significance of graphical representations in data exploration, commonly used techniques involve datasets that share numerous statistical properties yet produce distinct graphs.
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While a confusion matrix is undoubtedly a crucial tool in machine learning, the purpose of this post is not to explore its interpretation. Instead, I aim to share my personal journey of confusion matrix visualization.
Several years ago, during my initial machine learning project, I started by displaying a confusion matrix as raw numbers, keeping it simple yet informative. As I progressed, I explored different implementations, eventually using Seaborn’s heatmap() function to create more visually appealing representations of the confusion matrix.
Recently, I discovered the power of Sankey Diagrams and their interactive nature for visualizing the confusion matrix. The ability to hover over nodes and links to access numerical details added a new level of understanding to the evaluation process.
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Throughout my entire adult life, I have been captivated by the world of data analysis. As a student of applied and experimental physics in college, I delved into the intricacies of collecting and analyzing data from experiments, drawing valuable statistical inferences.
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