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README.md

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 # mark
 
-An IsolationForest anomaly detector over the [`stat`](https://huggingface.co/datasets/harpertoken/stat) macOS system-telemetry dataset. It learns what normal machine behaviour looks like from per-second CPU, memory, disk and network counters, and flags samples that do not fit. CPU only, scikit-learn only, no PyTorch and no transformer anywhere in the path. The saved bundle is a StandardScaler plus a 200-tree forest, eight features, a few hundred kilobytes.
+An IsolationForest anomaly detector over the [`stat`](https://huggingface.co/datasets/harpertoken/stat) macOS system-telemetry dataset. It learns what normal machine behaviour looks like from per-second CPU, memory, disk and network counters, and flags samples that do not fit. CPU only, scikit-learn only, no PyTorch and no transformer anywhere in the path. The saved bundle is a StandardScaler plus a 200-tree forest over eight features, and runs to about 1.9 MB.
 
 Training used the first 80 percent of the session in time order (4,951 rows) and held out the last 20 percent (1,238 rows). The disk and network columns in `stat` are since-boot counters, so they enter as per-interval differences, exactly as that card recommends. `cpu_temp` is null throughout and is dropped; per-core `cpu_usage` enters as its mean and max.