What visualization technique shows the correlation coefficient between variables to assess how close predicted values are to true values?

The correlation matrix is a powerful visualization technique that summarizes the relationships between multiple variables in a dataset. It displays the correlation coefficients, which quantify the strength and direction of the linear relationship between pairs of variables. Each coefficient ranges from -1 to 1, where values closer to 1 indicate a strong positive correlation, values closer to -1 indicate a strong negative correlation, and values around 0 suggest no correlation.

In the context of assessing how closely predicted values align with true values, a correlation matrix allows for a quick overview of how well different features relate to each other and to the target variable. This insight is critical for understanding the predictive power of features and guides decisions about feature selection and model assessment.

The other visualization techniques mentioned serve different purposes. A heatmap is often used to illustrate data through variations in color but does not inherently show correlation coefficients without additional context. A scatter plot visualizes the relationship between two numerical variables, providing insights about their correlation but is limited to pairwise comparisons and does not easily summarize multiple relationships. A box plot displays the distribution of a dataset and helps identify outliers, but it does not provide information on the correlation between variables. Each of these visualization methods has its strengths, but the correlation matrix specifically addresses the need