![]() MyPCAPredict applies PCA to new data using coeff and mu, and then predicts ratings using the transformed data. ![]() ScoreTest = Load trained classification model ![]() ![]() The points are scaled with respect to the maximum score value and maximum coefficient length, so only their relative locations can be determined from the plot.įunction label = myPCAPredict(XTest,coeff,mu) %#codegen % Transform data using PCA For example, points near the left edge of the plot have the lowest scores for the first principal component. This 2-D biplot also includes a point for each of the 13 observations, with coordinates indicating the score of each observation for the two principal components in the plot. The second principal component, which is on the vertical axis, has negative coefficients for the variables v 1, v 2, and v 4, and a positive coefficient for the variable v 3. The largest coefficient in the first principal component is the fourth, corresponding to the variable v 4. Therefore, vectors v 3 and v 4 are directed into the right half of the plot. For example, the first principal component, which is on the horizontal axis, has positive coefficients for the third and fourth variables. All four variables are represented in this biplot by a vector, and the direction and length of the vector indicate how each variable contributes to the two principal components in the plot.
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