Description
A heatmap is a two-dimensional graphical representation of data where individual values are represented by colors. It is particularly useful for visualizing numerical data organized in a table-like format.
A heatmap is a graphical representation of data where individual values are represented by colors. It is useful for visualizing numerical data and analyzing the correlation between features.
A heatmap is a visualization tool for analyzing the Correlation between features in a dataset. In the context of correlation analysis, a heatmap can display the correlation coefficients between different features in a dataset.
By using a heatmap, you can easily identify Multicollinearity and make informed decisions about which features to retain or remove, ultimately enhancing the performance and interpretability of your machine learning models.
Correlation Coefficients
The correlation coefficients range from -1 to 1:
- -1: Indicates a perfect negative correlation; if one attribute is present, the other is almost certainly absent.
- 0: Indicates no correlation; there is no dependence between the attributes.
- 1: Indicates a perfect positive correlation; if one attribute is present, the other is also certainly present.
Implementation in Python
In ML_Tools see: Heatmaps_Dendrograms.py