From the course: Machine Learning with Data Reduction in Excel, R, and Power BI
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Scree plot
From the course: Machine Learning with Data Reduction in Excel, R, and Power BI
Scree plot
- [Instructor] The PCA model axis lets us qualify the variances between data points in a consolidated view. Once we calculate the PCs on the axis, we need to determine which axis, PC1, PC2 or PC3 for example, should go into the PCA model. In our PCA plot, the X axis values account for the most variance, the Y axis values account for the second most variance, the Z axis values account for the third most variance and so on. But how can we compare the variants explicitly and compare it to the other dimensions in the projection space for the PCA model? One way is through a scree plot, which is a column chart that displays the amount of variance that each principle component accounts for in decreasing order. In our studio, one way we can get the information we need for the scree plot is through the summary function which we'll used to call the PCA variable. Now let's create a variable called outcome that we'll assign our PCA…
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