This visualization displays the results of an anomaly detection model on text data, showing true positives, true negatives, false positives, and false negatives in a two-dimensional space. The data undergoes TruncatedSVD reduction to 16 dimensions, followed by PCA and t-SNE projections to two dimensions. Points are colored by classification outcome—green, white, red, and orange—against a blue contour background representing the IsolationForest decision function. The code uses scikit-learn's TruncatedSVD, PCA, and TSNE, along with matplotlib's contourf and scatter for rendering.