③ Training tab

When you click ▶ Run Maxent at the bottom of the dock, focus shifts to the Training tab. A progress bar shows fold-by-fold progress and a log panel records what the model is doing in real time — useful for diagnosing slow runs, oversized rasters, or convergence issues.

What happens when you click Run

QMaxent runs the following pipeline; each step writes a status line to the log:

flowchart TD
    A["Sample background points"] --> B["Extract raster covariates<br/>(presence + background)"]
    B --> C["Apply maxnet auto-rule<br/>(or your manual feature choice)"]
    C --> D["Fit full Maxent model"]
    D --> E["Compute training ROC + AUC"]
    E --> F["Run spatial K-Fold CV<br/>(repeat fit per fold)"]
    F --> G["Compute pooled CV AUC"]
    G --> H["(optional) Jackknife loop:<br/>2N additional fits"]
    H --> I["Save .pkl + results.xlsx"]

The fit is performed by elapid's MaxentModel, a faithful Python port of the original Java MaxEnt (Phillips et al. 2006; Phillips et al. 2017) that delegates the regularized likelihood maximisation to scikit-learn (Pedregosa et al. 2011). The result is numerically very close to Java MaxEnt for identical inputs and identical hyperparameters — the residual difference comes mostly from how each implementation handles ties in the optimisation step.

Reading the log

A typical successful run produces a log like:

text → 10,000 background points sampled Extracting raster covariates for presence points… Extracting raster covariates for background points… → Presence: 116, Background: 9,997 → Feature types: ['linear', 'quadratic', 'product', 'hinge', 'threshold'] Training MaxentModel… → Model training complete → Model saved: …/model.pkl Computing ROC curve… → Training AUC = 0.9562 Running cross-validation… Fold 1: 22 test presences, AUC = 0.7453 Fold 2: 21 test presences, AUC = 0.7839 Fold 3: 39 test presences, AUC = 0.8097 Fold 4: 26 test presences, AUC = 0.8614 Fold 5: 8 test presences, AUC = 0.5903 → CV AUC = 0.7581 ± 0.0920 (n=5 fold(s))

A few patterns to recognise:

• Background = 9,997 instead of 10,000 — points that fell on NoData cells were dropped. Normal and not a cause for concern unless the loss is large (>5 %).
• Per-fold AUC well below the mean — for example Fold 5 above (AUC = 0.59 with only 8 presences). Geographic K-Fold deliberately produces uneven folds, and a small fold can land on an atypical region of the study area. The pooled mean smooths this out.
• Train AUC ≫ CV AUC — the spatial-CV gap. A good sign in the sense that it tells you the model has not been silently over-fitted to the training presences (Roberts et al. 2017). A train AUC of 0.95 with a CV AUC of 0.55 is the warning sign — the CV is telling you the model does not generalise.

Completion

When the run finishes, you see the green Done state:

The dock's footer status bar populates with train AUC=… CV AUC=… and the ④ Results tab unlocks.

Cancelling a run

Click Cancel to abort. QMaxent terminates the current fold cleanly, discards the in-memory model, and leaves disk outputs untouched. You can restart by clicking ▶ Run Maxent again.

Common warnings

Message	Meaning	Action
⚠ Removed 312 presences with NoData covariate values	Some points fell on NoData cells	Inspect the raster stack; may indicate the wrong CRS or a clipped raster
⚠ Background points cap reduced from 10,000 to N	Study area too small for the requested background size	Reduce the background-points field on Data tab
⚠ Convergence not reached after 500 iterations	Hard-to-fit model (often very few presences)	Increase the regularization multiplier or simplify the feature set

Next

Move to the ④ Results tab to inspect response curves, Jackknife importance, and run a spatial projection.